forked from scikit-learn/scikit-learn.github.io
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathmetadata_routing.html
More file actions
1114 lines (886 loc) · 89.7 KB
/
Copy pathmetadata_routing.html
File metadata and controls
1114 lines (886 loc) · 89.7 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<html lang="en" data-content_root="./" >
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<meta property="og:title" content="4. Metadata Routing" />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://scikit-learn/stable/metadata_routing.html" />
<meta property="og:site_name" content="scikit-learn" />
<meta property="og:description" content="This guide demonstrates how metadata can be routed and passed between objects in scikit-learn. If you are developing a scikit-learn compatible estimator or meta-estimator, you can check our related..." />
<meta property="og:image" content="https://scikit-learn.org/stable/_static/scikit-learn-logo-notext.png" />
<meta property="og:image:alt" content="scikit-learn" />
<meta name="description" content="This guide demonstrates how metadata can be routed and passed between objects in scikit-learn. If you are developing a scikit-learn compatible estimator or meta-estimator, you can check our related..." />
<title>4. Metadata Routing — scikit-learn 1.8.0 documentation</title>
<script data-cfasync="false">
document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
</script>
<!--
this give us a css class that will be invisible only if js is disabled
-->
<noscript>
<style>
.pst-js-only { display: none !important; }
</style>
</noscript>
<!-- Loaded before other Sphinx assets -->
<link href="_static/styles/theme.css?digest=8878045cc6db502f8baf" rel="stylesheet" />
<link href="_static/styles/pydata-sphinx-theme.css?digest=8878045cc6db502f8baf" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=8f2a1f02" />
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
<link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
<link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
<link rel="stylesheet" type="text/css" href="_static/jupyterlite_sphinx.css?v=8ee2c72c" />
<link rel="stylesheet" type="text/css" href="_static/sg_gallery.css?v=d2d258e8" />
<link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css?v=f4aeca0c" />
<link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css?v=2082cf3c" />
<link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
<link rel="stylesheet" type="text/css" href="_static/styles/colors.css?v=cc94ab7d" />
<link rel="stylesheet" type="text/css" href="_static/styles/custom.css?v=eec3fc85" />
<!-- So that users can add custom icons -->
<script src="_static/scripts/fontawesome.js?digest=8878045cc6db502f8baf"></script>
<!-- Pre-loaded scripts that we'll load fully later -->
<link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=8878045cc6db502f8baf" />
<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=8878045cc6db502f8baf" />
<script src="_static/documentation_options.js?v=b51e6972"></script>
<script src="_static/doctools.js?v=9bcbadda"></script>
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
<script src="_static/clipboard.min.js?v=a7894cd8"></script>
<script src="_static/copybutton.js?v=97f0b27d"></script>
<script src="_static/jupyterlite_sphinx.js?v=96e329c5"></script>
<script src="_static/design-tabs.js?v=f930bc37"></script>
<script data-domain="scikit-learn.org" defer="defer" src="https://views.scientific-python.org/js/script.js"></script>
<script>DOCUMENTATION_OPTIONS.pagename = 'metadata_routing';</script>
<script>
DOCUMENTATION_OPTIONS.theme_version = '0.16.1';
DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://scikit-learn.org/dev/_static/versions.json';
DOCUMENTATION_OPTIONS.theme_switcher_version_match = '1.8.0';
DOCUMENTATION_OPTIONS.show_version_warning_banner =
true;
</script>
<script src="_static/scripts/dropdown.js?v=d6825577"></script>
<script src="_static/scripts/version-switcher.js?v=a6dd8357"></script>
<script src="_static/scripts/sg_plotly_resize.js?v=2167d4db"></script>
<script src="_static/scripts/theme-observer.js?v=bf5fb916"></script>
<link rel="canonical" href="https://scikit-learn.org/stable/metadata_routing.html" />
<link rel="icon" href="_static/favicon.ico"/>
<link rel="author" title="About these documents" href="about.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="5. Inspection" href="inspection.html" />
<link rel="prev" title="3.5. Validation curves: plotting scores to evaluate models" href="modules/learning_curve.html" />
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
<meta name="docsearch:version" content="1.8" />
</head>
<body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
<div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
<div id="pst-scroll-pixel-helper"></div>
<button type="button" class="btn rounded-pill" id="pst-back-to-top">
<i class="fa-solid fa-arrow-up"></i>Back to top</button>
<dialog id="pst-search-dialog">
<form class="bd-search d-flex align-items-center"
action="search.html"
method="get">
<i class="fa-solid fa-magnifying-glass"></i>
<input type="search"
class="form-control"
name="q"
placeholder="Search the docs ..."
aria-label="Search the docs ..."
autocomplete="off"
autocorrect="off"
autocapitalize="off"
spellcheck="false"/>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
</form>
</dialog>
<div class="pst-async-banner-revealer d-none">
<aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
</div>
<header class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
<div class="bd-header__inner bd-page-width">
<button class="pst-navbar-icon sidebar-toggle primary-toggle" aria-label="Site navigation">
<span class="fa-solid fa-bars"></span>
</button>
<div class=" navbar-header-items__start">
<div class="navbar-item">
<a class="navbar-brand logo" href="index.html">
<img src="_static/scikit-learn-logo-without-subtitle.svg" class="logo__image only-light" alt="scikit-learn homepage"/>
<img src="_static/scikit-learn-logo-without-subtitle.svg" class="logo__image only-dark pst-js-only" alt="scikit-learn homepage"/>
</a></div>
</div>
<div class=" navbar-header-items">
<div class="me-auto navbar-header-items__center">
<div class="navbar-item">
<nav>
<ul class="bd-navbar-elements navbar-nav">
<li class="nav-item ">
<a class="nav-link nav-internal" href="install.html">
Install
</a>
</li>
<li class="nav-item current active">
<a class="nav-link nav-internal" href="user_guide.html">
User Guide
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="api/index.html">
API
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="auto_examples/index.html">
Examples
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://blog.scikit-learn.org/">
Community
</a>
</li>
<li class="nav-item dropdown">
<button class="btn dropdown-toggle nav-item" type="button"
data-bs-toggle="dropdown" aria-expanded="false"
aria-controls="pst-nav-more-links">
More
</button>
<ul id="pst-nav-more-links" class="dropdown-menu">
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="getting_started.html">
Getting Started
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="whats_new.html">
Release History
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="glossary.html">
Glossary
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-external" href="https://scikit-learn.org/dev/developers/index.html">
Development
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="faq.html">
FAQ
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="support.html">
Support
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="related_projects.html">
Related Projects
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="roadmap.html">
Roadmap
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="governance.html">
Governance
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="about.html">
About us
</a>
</li>
</ul>
</li>
</ul>
</nav></div>
</div>
<div class="navbar-header-items__end">
<div class="navbar-item navbar-persistent--container">
<button class="btn btn-sm pst-navbar-icon search-button search-button__button pst-js-only" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass fa-lg"></i>
</button>
</div>
<div class="navbar-item">
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light" title="Light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark" title="Dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto" title="System Settings"></i>
</button></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/scikit-learn/scikit-learn" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i>
<span class="sr-only">GitHub</span></a>
</li>
</ul></div>
<div class="navbar-item">
<div class="version-switcher__container dropdown pst-js-only">
<button id="pst-version-switcher-button-2"
type="button"
class="version-switcher__button btn btn-sm dropdown-toggle"
data-bs-toggle="dropdown"
aria-haspopup="listbox"
aria-controls="pst-version-switcher-list-2"
aria-label="Version switcher list"
>
Choose version <!-- this text may get changed later by javascript -->
<span class="caret"></span>
</button>
<div id="pst-version-switcher-list-2"
class="version-switcher__menu dropdown-menu list-group-flush py-0"
role="listbox" aria-labelledby="pst-version-switcher-button-2">
<!-- dropdown will be populated by javascript on page load -->
</div>
</div></div>
</div>
</div>
<div class="navbar-persistent--mobile">
<button class="btn btn-sm pst-navbar-icon search-button search-button__button pst-js-only" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass fa-lg"></i>
</button>
</div>
<button class="pst-navbar-icon sidebar-toggle secondary-toggle" aria-label="On this page">
<span class="fa-solid fa-outdent"></span>
</button>
</div>
</header>
<div class="bd-container">
<div class="bd-container__inner bd-page-width">
<dialog id="pst-primary-sidebar-modal"></dialog>
<div id="pst-primary-sidebar" class="bd-sidebar-primary bd-sidebar">
<div class="sidebar-header-items sidebar-primary__section">
<div class="sidebar-header-items__center">
<div class="navbar-item">
<nav>
<ul class="bd-navbar-elements navbar-nav">
<li class="nav-item ">
<a class="nav-link nav-internal" href="install.html">
Install
</a>
</li>
<li class="nav-item current active">
<a class="nav-link nav-internal" href="user_guide.html">
User Guide
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="api/index.html">
API
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="auto_examples/index.html">
Examples
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://blog.scikit-learn.org/">
Community
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="getting_started.html">
Getting Started
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="whats_new.html">
Release History
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="glossary.html">
Glossary
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://scikit-learn.org/dev/developers/index.html">
Development
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="faq.html">
FAQ
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="support.html">
Support
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="related_projects.html">
Related Projects
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="roadmap.html">
Roadmap
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="governance.html">
Governance
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="about.html">
About us
</a>
</li>
</ul>
</nav></div>
</div>
<div class="sidebar-header-items__end">
<div class="navbar-item">
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light" title="Light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark" title="Dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto" title="System Settings"></i>
</button></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/scikit-learn/scikit-learn" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i>
<span class="sr-only">GitHub</span></a>
</li>
</ul></div>
<div class="navbar-item">
<div class="version-switcher__container dropdown pst-js-only">
<button id="pst-version-switcher-button-3"
type="button"
class="version-switcher__button btn btn-sm dropdown-toggle"
data-bs-toggle="dropdown"
aria-haspopup="listbox"
aria-controls="pst-version-switcher-list-3"
aria-label="Version switcher list"
>
Choose version <!-- this text may get changed later by javascript -->
<span class="caret"></span>
</button>
<div id="pst-version-switcher-list-3"
class="version-switcher__menu dropdown-menu list-group-flush py-0"
role="listbox" aria-labelledby="pst-version-switcher-button-3">
<!-- dropdown will be populated by javascript on page load -->
</div>
</div></div>
</div>
</div>
<div class="sidebar-primary-items__start sidebar-primary__section">
<div class="sidebar-primary-item">
<nav class="bd-docs-nav bd-links"
aria-label="Section Navigation">
<p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
<div class="bd-toc-item navbar-nav"><ul class="current nav bd-sidenav">
<li class="toctree-l1 has-children"><a class="reference internal" href="supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html">1.1. Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_ridge.html">1.3. Kernel ridge regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html">1.4. Support Vector Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html">1.5. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html">1.6. Nearest Neighbors</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html">1.7. Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_decomposition.html">1.8. Cross decomposition</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html">1.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html">1.10. Decision Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multiclass.html">1.12. Multiclass and multioutput algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html">1.13. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/mixture.html">2.1. Gaussian mixture models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html">2.2. Manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/density.html">2.8. Density Estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="model_selection.html">3. Model selection and evaluation</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
</ul>
</details></li>
<li class="toctree-l1 current active"><a class="current reference internal" href="#">4. Metadata Routing</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="visualizations.html">6. Visualizations</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html">7.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html">7.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html">7.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html">7.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html">7.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html">8.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html">8.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html">8.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/computational_performance.html">9.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="model_persistence.html">10. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/array_api.html">12.1. Array API support (experimental)</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="machine_learning_map.html">13. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="presentations.html">14. External Resources, Videos and Talks</a></li>
</ul>
</div>
</nav></div>
</div>
<div class="sidebar-primary-items__end sidebar-primary__section">
</div>
</div>
<main id="main-content" class="bd-main" role="main">
<div class="bd-content">
<div class="bd-article-container">
<div class="bd-header-article d-print-none">
<div class="header-article-items header-article__inner">
<div class="header-article-items__start">
<div class="header-article-item">
<nav aria-label="Breadcrumb" class="d-print-none">
<ul class="bd-breadcrumbs">
<li class="breadcrumb-item breadcrumb-home">
<a href="index.html" class="nav-link" aria-label="Home">
<i class="fa-solid fa-home"></i>
</a>
</li>
<li class="breadcrumb-item"><a href="user_guide.html" class="nav-link">User Guide</a></li>
<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">4. </span>Metadata Routing</span></li>
</ul>
</nav>
</div>
</div>
</div>
</div>
<div id="searchbox"></div>
<article class="bd-article">
<section id="metadata-routing">
<span id="id1"></span><h1><span class="section-number">4. </span>Metadata Routing<a class="headerlink" href="#metadata-routing" title="Link to this heading">#</a></h1>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The Metadata Routing API is experimental, and is not yet implemented for all
estimators. Please refer to the <a class="reference internal" href="#metadata-routing-models"><span class="std std-ref">list of supported and unsupported
models</span></a> for more information. It may change without
the usual deprecation cycle. By default this feature is not enabled. You can
enable it by setting the <code class="docutils literal notranslate"><span class="pre">enable_metadata_routing</span></code> flag to
<code class="docutils literal notranslate"><span class="pre">True</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">sklearn</span>
<span class="gp">>>> </span><span class="n">sklearn</span><span class="o">.</span><span class="n">set_config</span><span class="p">(</span><span class="n">enable_metadata_routing</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>Note that the methods and requirements introduced in this document are only
relevant if you want to pass <a class="reference internal" href="glossary.html#term-metadata"><span class="xref std std-term">metadata</span></a> (e.g. <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>) to a method.
If you’re only passing <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> and no other parameter / metadata to
methods such as <a class="reference internal" href="glossary.html#term-fit"><span class="xref std std-term">fit</span></a>, <a class="reference internal" href="glossary.html#term-transform"><span class="xref std std-term">transform</span></a>, etc., then you don’t need to set
anything.</p>
</div>
<p>This guide demonstrates how <a class="reference internal" href="glossary.html#term-metadata"><span class="xref std std-term">metadata</span></a> can be routed and passed between objects in
scikit-learn. If you are developing a scikit-learn compatible estimator or
meta-estimator, you can check our related developer guide:
<a class="reference internal" href="auto_examples/miscellaneous/plot_metadata_routing.html#sphx-glr-auto-examples-miscellaneous-plot-metadata-routing-py"><span class="std std-ref">Metadata Routing</span></a>.</p>
<p>Metadata is data that an estimator, scorer, or CV splitter takes into account if the
user explicitly passes it as a parameter. For instance, <a class="reference internal" href="modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans"><code class="xref py py-class docutils literal notranslate"><span class="pre">KMeans</span></code></a> accepts
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> in its <code class="docutils literal notranslate"><span class="pre">fit()</span></code> method and considers it to calculate its centroids.
<code class="docutils literal notranslate"><span class="pre">classes</span></code> are consumed by some classifiers and <code class="docutils literal notranslate"><span class="pre">groups</span></code> are used in some splitters, but
any data that is passed into an object’s methods apart from X and y can be considered as
metadata. Prior to scikit-learn version 1.3, there was no single API for passing
metadata like that if these objects were used in conjunction with other objects, e.g. a
scorer accepting <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> inside a <a class="reference internal" href="modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a>.</p>
<p>With the Metadata Routing API, we can transfer metadata to estimators, scorers, and CV
splitters using <a class="reference internal" href="glossary.html#term-meta-estimators"><span class="xref std std-term">meta-estimators</span></a> (such as <a class="reference internal" href="modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> or
<a class="reference internal" href="modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a>) or functions such as
<a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> which route data to other objects. In order to
pass metadata to a method like <code class="docutils literal notranslate"><span class="pre">fit</span></code> or <code class="docutils literal notranslate"><span class="pre">score</span></code>, the object consuming the metadata,
must <em>request</em> it. This is done via <code class="docutils literal notranslate"><span class="pre">set_{method}_request()</span></code> methods, where <code class="docutils literal notranslate"><span class="pre">{method}</span></code>
is substituted by the name of the method that requests the metadata. For instance,
estimators that use the metadata in their <code class="docutils literal notranslate"><span class="pre">fit()</span></code> method would use <code class="docutils literal notranslate"><span class="pre">set_fit_request()</span></code>,
and scorers would use <code class="docutils literal notranslate"><span class="pre">set_score_request()</span></code>. These methods allow us to specify which
metadata to request, for instance <code class="docutils literal notranslate"><span class="pre">set_fit_request(sample_weight=True)</span></code>.</p>
<p>For grouped splitters such as <a class="reference internal" href="modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupKFold</span></code></a>, a
<code class="docutils literal notranslate"><span class="pre">groups</span></code> parameter is requested by default. This is best demonstrated by the
following examples.</p>
<section id="usage-examples">
<h2><span class="section-number">4.1. </span>Usage Examples<a class="headerlink" href="#usage-examples" title="Link to this heading">#</a></h2>
<p>Here we present a few examples to show some common use-cases. Our goal is to pass
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> and <code class="docutils literal notranslate"><span class="pre">groups</span></code> through <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a>, which
routes the metadata to <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> and to a custom scorer
made with <a class="reference internal" href="modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer</span></code></a>, both of which <em>can</em> use the metadata in their
methods. In these examples we want to individually set whether to use the metadata
within the different <a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumers</span></a>.</p>
<p>The examples in this section require the following imports and data:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_scorer</span><span class="p">,</span> <span class="n">accuracy_score</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegressionCV</span><span class="p">,</span> <span class="n">LogisticRegression</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">cross_validate</span><span class="p">,</span> <span class="n">GridSearchCV</span><span class="p">,</span> <span class="n">GroupKFold</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">SelectKBest</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="gp">>>> </span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">4</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">my_groups</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">my_weights</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n_samples</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">my_other_weights</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n_samples</span><span class="p">)</span>
</pre></div>
</div>
<section id="weighted-scoring-and-fitting">
<h3><span class="section-number">4.1.1. </span>Weighted scoring and fitting<a class="headerlink" href="#weighted-scoring-and-fitting" title="Link to this heading">#</a></h3>
<p>The splitter used internally in <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>,
<a class="reference internal" href="modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupKFold</span></code></a>, requests <code class="docutils literal notranslate"><span class="pre">groups</span></code> by default. However, we need
to explicitly request <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> for it and for our custom scorer by specifying
<code class="docutils literal notranslate"><span class="pre">sample_weight=True</span></code> in <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>’s <code class="docutils literal notranslate"><span class="pre">set_fit_request()</span></code>
method and in <a class="reference internal" href="modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer</span></code></a>’s <code class="docutils literal notranslate"><span class="pre">set_score_request()</span></code> method. Both
<a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumers</span></a> know how to use <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> in their <code class="docutils literal notranslate"><span class="pre">fit()</span></code> or
<code class="docutils literal notranslate"><span class="pre">score()</span></code> methods. We can then pass the metadata in
<a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> which will route it to any active consumers:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_acc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">use_legacy_attributes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">lr</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s2">"sample_weight"</span><span class="p">:</span> <span class="n">my_weights</span><span class="p">,</span> <span class="s2">"groups"</span><span class="p">:</span> <span class="n">my_groups</span><span class="p">},</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
<p>Note that in this example, <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> routes <code class="docutils literal notranslate"><span class="pre">my_weights</span></code>
to both the scorer and <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>.</p>
<p>If we would pass <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> in the params of
<a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a>, but not set any object to request it,
<code class="docutils literal notranslate"><span class="pre">UnsetMetadataPassedError</span></code> would be raised, hinting to us that we need to explicitly set
where to route it. The same applies if <code class="docutils literal notranslate"><span class="pre">params={"sample_weights":</span> <span class="pre">my_weights,</span> <span class="pre">...}</span></code>
were passed (note the typo, i.e. <code class="docutils literal notranslate"><span class="pre">weights</span></code> instead of <code class="docutils literal notranslate"><span class="pre">weight</span></code>), since
<code class="docutils literal notranslate"><span class="pre">sample_weights</span></code> was not requested by any of its underlying objects.</p>
</section>
<section id="weighted-scoring-and-unweighted-fitting">
<h3><span class="section-number">4.1.2. </span>Weighted scoring and unweighted fitting<a class="headerlink" href="#weighted-scoring-and-unweighted-fitting" title="Link to this heading">#</a></h3>
<p>When passing metadata such as <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> into a <a class="reference internal" href="glossary.html#term-router"><span class="xref std std-term">router</span></a>
(<a class="reference internal" href="glossary.html#term-meta-estimators"><span class="xref std std-term">meta-estimators</span></a> or routing function), all <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> <a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumers</span></a> require weights to be either explicitly requested or explicitly not
requested (i.e. <code class="docutils literal notranslate"><span class="pre">True</span></code> or <code class="docutils literal notranslate"><span class="pre">False</span></code>). Thus, to perform an unweighted fit, we need to
configure <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> to not request sample weights, so
that <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> does not pass the weights along:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_acc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span> <span class="n">use_legacy_attributes</span><span class="o">=</span><span class="kc">False</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">lr</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s2">"sample_weight"</span><span class="p">:</span> <span class="n">my_weights</span><span class="p">,</span> <span class="s2">"groups"</span><span class="p">:</span> <span class="n">my_groups</span><span class="p">},</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
<p>If <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV.set_fit_request" title="sklearn.linear_model.LogisticRegressionCV.set_fit_request"><code class="xref py py-meth docutils literal notranslate"><span class="pre">linear_model.LogisticRegressionCV.set_fit_request</span></code></a> had not been called,
<a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> would raise an error because <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>
is passed but <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> would not be explicitly
configured to recognize the weights.</p>
</section>
<section id="unweighted-feature-selection">
<h3><span class="section-number">4.1.3. </span>Unweighted feature selection<a class="headerlink" href="#unweighted-feature-selection" title="Link to this heading">#</a></h3>
<p>Routing metadata is only possible if the object’s method knows how to use the metadata,
which in most cases means they have it as an explicit parameter. Only then we can set
request values for metadata using <code class="docutils literal notranslate"><span class="pre">set_fit_request(sample_weight=True)</span></code>, for instance.
This makes the object a <a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumer</span></a>.</p>
<p>Unlike <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>,
<a class="reference internal" href="modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest" title="sklearn.feature_selection.SelectKBest"><code class="xref py py-class docutils literal notranslate"><span class="pre">SelectKBest</span></code></a> can’t consume weights and therefore no request
value for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> on its instance is set and <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is not routed
to it:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_acc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span> <span class="n">use_legacy_attributes</span><span class="o">=</span><span class="kc">False</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">sel</span> <span class="o">=</span> <span class="n">SelectKBest</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">pipe</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">sel</span><span class="p">,</span> <span class="n">lr</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">pipe</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">params</span><span class="o">=</span><span class="p">{</span><span class="s2">"sample_weight"</span><span class="p">:</span> <span class="n">my_weights</span><span class="p">,</span> <span class="s2">"groups"</span><span class="p">:</span> <span class="n">my_groups</span><span class="p">},</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="different-scoring-and-fitting-weights">
<h3><span class="section-number">4.1.4. </span>Different scoring and fitting weights<a class="headerlink" href="#different-scoring-and-fitting-weights" title="Link to this heading">#</a></h3>
<p>Despite <a class="reference internal" href="modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer</span></code></a> and
<a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> both expecting the key
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>, we can use aliases to pass different weights to different
consumers. In this example, we pass <code class="docutils literal notranslate"><span class="pre">scoring_weight</span></code> to the scorer, and
<code class="docutils literal notranslate"><span class="pre">fitting_weight</span></code> to <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_acc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">sample_weight</span><span class="o">=</span><span class="s2">"scoring_weight"</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span> <span class="n">use_legacy_attributes</span><span class="o">=</span><span class="kc">False</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="s2">"fitting_weight"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">lr</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">params</span><span class="o">=</span><span class="p">{</span>
<span class="gp">... </span> <span class="s2">"scoring_weight"</span><span class="p">:</span> <span class="n">my_weights</span><span class="p">,</span>
<span class="gp">... </span> <span class="s2">"fitting_weight"</span><span class="p">:</span> <span class="n">my_other_weights</span><span class="p">,</span>
<span class="gp">... </span> <span class="s2">"groups"</span><span class="p">:</span> <span class="n">my_groups</span><span class="p">,</span>
<span class="gp">... </span> <span class="p">},</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
</section>
</section>
<section id="api-interface">
<h2><span class="section-number">4.2. </span>API Interface<a class="headerlink" href="#api-interface" title="Link to this heading">#</a></h2>
<p>A <a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumer</span></a> is an object (estimator, meta-estimator, scorer, splitter) which
accepts and uses some <a class="reference internal" href="glossary.html#term-metadata"><span class="xref std std-term">metadata</span></a> in at least one of its methods (for instance
<code class="docutils literal notranslate"><span class="pre">fit</span></code>, <code class="docutils literal notranslate"><span class="pre">predict</span></code>, <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code>, <code class="docutils literal notranslate"><span class="pre">transform</span></code>, <code class="docutils literal notranslate"><span class="pre">score</span></code>, <code class="docutils literal notranslate"><span class="pre">split</span></code>).
Meta-estimators which only forward the metadata to other objects (child estimators,
scorers, or splitters) and don’t use the metadata themselves are not consumers.
(Meta-)Estimators which route metadata to other objects are <a class="reference internal" href="glossary.html#term-router"><span class="xref std std-term">routers</span></a>.
A(n) (meta-)estimator can be a <a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumer</span></a> and a <a class="reference internal" href="glossary.html#term-router"><span class="xref std std-term">router</span></a> at the same time.
(Meta-)Estimators and splitters expose a <code class="docutils literal notranslate"><span class="pre">set_{method}_request</span></code> method for each method
which accepts at least one metadata. For instance, if an estimator supports
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> in <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">score</span></code>, it exposes
<code class="docutils literal notranslate"><span class="pre">estimator.set_fit_request(sample_weight=value)</span></code> and
<code class="docutils literal notranslate"><span class="pre">estimator.set_score_request(sample_weight=value)</span></code>. Here <code class="docutils literal notranslate"><span class="pre">value</span></code> can be:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: method requests a <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>. This means if the metadata is provided,
it will be used, otherwise no error is raised.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: method does not request a <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: router will raise an error if <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is passed. This is in almost
all cases the default value when an object is instantiated and ensures the user sets
the metadata requests explicitly when a metadata is passed. The only exception are
<code class="docutils literal notranslate"><span class="pre">Group*Fold</span></code> splitters.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"param_name"</span></code>: alias for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> if we want to pass different weights to
different consumers. If aliasing is used the meta-estimator should not forward
<code class="docutils literal notranslate"><span class="pre">"param_name"</span></code> to the consumer, but <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> instead, because the consumer
will expect a param called <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>. This means the mapping between the
metadata required by the object, e.g. <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> and the variable name provided
by the user, e.g. <code class="docutils literal notranslate"><span class="pre">my_weights</span></code> is done at the router level, and not by the consuming
object itself.</p></li>
</ul>
<p>Metadata are requested in the same way for scorers using <code class="docutils literal notranslate"><span class="pre">set_score_request</span></code>.</p>
<p>If a metadata, e.g. <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>, is passed by the user, the metadata request for
all objects which potentially can consume <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> should be set by the user,
otherwise an error is raised by the router object. For example, the following code
raises an error, since it hasn’t been explicitly specified whether <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>
should be passed to the estimator’s scorer or not:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"C"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">try</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">GridSearchCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">estimator</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">param_grid</span>
<span class="gp">... </span> <span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">my_weights</span><span class="p">)</span>
<span class="gp">... </span><span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="go">[sample_weight] are passed but are not explicitly set as requested or not</span>
<span class="go">requested for LogisticRegression.score, which is used within GridSearchCV.fit.</span>
<span class="go">Call `LogisticRegression.set_score_request({metadata}=True/False)` for each metadata</span>
<span class="go">you want to request/ignore. See the Metadata Routing User guide</span>
<span class="go"><https://scikit-learn.org/stable/metadata_routing.html> for more information.</span>
</pre></div>
</div>
<p>The issue can be fixed by explicitly setting the request value:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
<p>At the end of the <strong>Usage Examples</strong> section, we disable the configuration flag for
metadata routing:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">sklearn</span><span class="o">.</span><span class="n">set_config</span><span class="p">(</span><span class="n">enable_metadata_routing</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="metadata-routing-support-status">
<span id="metadata-routing-models"></span><h2><span class="section-number">4.3. </span>Metadata Routing Support Status<a class="headerlink" href="#metadata-routing-support-status" title="Link to this heading">#</a></h2>
<p>All consumers (i.e. simple estimators which only consume metadata and don’t
route them) support metadata routing, meaning they can be used inside
meta-estimators which support metadata routing. However, development of support
for metadata routing for meta-estimators is in progress, and here is a list of
meta-estimators and tools which support and don’t yet support metadata routing.</p>
<p>Meta-estimators and functions supporting metadata routing:</p>
<ul class="simple">
<li><p><a class="reference internal" href="modules/generated/sklearn.calibration.CalibratedClassifierCV.html#sklearn.calibration.CalibratedClassifierCV" title="sklearn.calibration.CalibratedClassifierCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.calibration.CalibratedClassifierCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer" title="sklearn.compose.ColumnTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.compose.ColumnTransformer</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.compose.TransformedTargetRegressor.html#sklearn.compose.TransformedTargetRegressor" title="sklearn.compose.TransformedTargetRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.compose.TransformedTargetRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.covariance.GraphicalLassoCV.html#sklearn.covariance.GraphicalLassoCV" title="sklearn.covariance.GraphicalLassoCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.covariance.GraphicalLassoCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.StackingClassifier.html#sklearn.ensemble.StackingClassifier" title="sklearn.ensemble.StackingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.StackingClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.StackingRegressor.html#sklearn.ensemble.StackingRegressor" title="sklearn.ensemble.StackingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.StackingRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.VotingClassifier.html#sklearn.ensemble.VotingClassifier" title="sklearn.ensemble.VotingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.VotingClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.VotingRegressor.html#sklearn.ensemble.VotingRegressor" title="sklearn.ensemble.VotingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.VotingRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.BaggingClassifier.html#sklearn.ensemble.BaggingClassifier" title="sklearn.ensemble.BaggingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.BaggingClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.BaggingRegressor.html#sklearn.ensemble.BaggingRegressor" title="sklearn.ensemble.BaggingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.BaggingRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.feature_selection.RFE.html#sklearn.feature_selection.RFE" title="sklearn.feature_selection.RFE"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.feature_selection.RFE</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.feature_selection.RFECV.html#sklearn.feature_selection.RFECV" title="sklearn.feature_selection.RFECV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.feature_selection.RFECV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.feature_selection.SelectFromModel.html#sklearn.feature_selection.SelectFromModel" title="sklearn.feature_selection.SelectFromModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.feature_selection.SelectFromModel</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.feature_selection.SequentialFeatureSelector.html#sklearn.feature_selection.SequentialFeatureSelector" title="sklearn.feature_selection.SequentialFeatureSelector"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.feature_selection.SequentialFeatureSelector</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer" title="sklearn.impute.IterativeImputer"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.impute.IterativeImputer</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.ElasticNetCV.html#sklearn.linear_model.ElasticNetCV" title="sklearn.linear_model.ElasticNetCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.ElasticNetCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.LarsCV.html#sklearn.linear_model.LarsCV" title="sklearn.linear_model.LarsCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LarsCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.LassoCV.html#sklearn.linear_model.LassoCV" title="sklearn.linear_model.LassoCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LassoCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.LassoLarsCV.html#sklearn.linear_model.LassoLarsCV" title="sklearn.linear_model.LassoLarsCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LassoLarsCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LogisticRegressionCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.html#sklearn.linear_model.MultiTaskElasticNetCV" title="sklearn.linear_model.MultiTaskElasticNetCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.MultiTaskElasticNetCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.MultiTaskLassoCV.html#sklearn.linear_model.MultiTaskLassoCV" title="sklearn.linear_model.MultiTaskLassoCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.MultiTaskLassoCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html#sklearn.linear_model.OrthogonalMatchingPursuitCV" title="sklearn.linear_model.OrthogonalMatchingPursuitCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.OrthogonalMatchingPursuitCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.RANSACRegressor.html#sklearn.linear_model.RANSACRegressor" title="sklearn.linear_model.RANSACRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.RANSACRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.RidgeClassifierCV.html#sklearn.linear_model.RidgeClassifierCV" title="sklearn.linear_model.RidgeClassifierCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.RidgeClassifierCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.linear_model.RidgeCV.html#sklearn.linear_model.RidgeCV" title="sklearn.linear_model.RidgeCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.RidgeCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.GridSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.HalvingGridSearchCV.html#sklearn.model_selection.HalvingGridSearchCV" title="sklearn.model_selection.HalvingGridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.HalvingGridSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.HalvingRandomSearchCV.html#sklearn.model_selection.HalvingRandomSearchCV" title="sklearn.model_selection.HalvingRandomSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.HalvingRandomSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.RandomizedSearchCV</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.permutation_test_score.html#sklearn.model_selection.permutation_test_score" title="sklearn.model_selection.permutation_test_score"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.permutation_test_score</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.model_selection.cross_validate</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.model_selection.cross_val_score</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.cross_val_predict.html#sklearn.model_selection.cross_val_predict" title="sklearn.model_selection.cross_val_predict"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.model_selection.cross_val_predict</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.learning_curve.html#sklearn.model_selection.learning_curve" title="sklearn.model_selection.learning_curve"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.learning_curve</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.model_selection.validation_curve.html#sklearn.model_selection.validation_curve" title="sklearn.model_selection.validation_curve"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.model_selection.validation_curve</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multiclass.OneVsOneClassifier.html#sklearn.multiclass.OneVsOneClassifier" title="sklearn.multiclass.OneVsOneClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multiclass.OneVsOneClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multiclass.OneVsRestClassifier.html#sklearn.multiclass.OneVsRestClassifier" title="sklearn.multiclass.OneVsRestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multiclass.OneVsRestClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multiclass.OutputCodeClassifier.html#sklearn.multiclass.OutputCodeClassifier" title="sklearn.multiclass.OutputCodeClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multiclass.OutputCodeClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multioutput.ClassifierChain.html#sklearn.multioutput.ClassifierChain" title="sklearn.multioutput.ClassifierChain"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multioutput.ClassifierChain</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multioutput.MultiOutputClassifier.html#sklearn.multioutput.MultiOutputClassifier" title="sklearn.multioutput.MultiOutputClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multioutput.MultiOutputClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multioutput.MultiOutputRegressor</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.multioutput.RegressorChain.html#sklearn.multioutput.RegressorChain" title="sklearn.multioutput.RegressorChain"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.multioutput.RegressorChain</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.pipeline.FeatureUnion.html#sklearn.pipeline.FeatureUnion" title="sklearn.pipeline.FeatureUnion"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.pipeline.FeatureUnion</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.pipeline.Pipeline</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.html#sklearn.semi_supervised.SelfTrainingClassifier" title="sklearn.semi_supervised.SelfTrainingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.semi_supervised.SelfTrainingClassifier</span></code></a></p></li>
</ul>
<p>Meta-estimators and tools not supporting metadata routing yet:</p>
<ul class="simple">
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier" title="sklearn.ensemble.AdaBoostClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.AdaBoostClassifier</span></code></a></p></li>
<li><p><a class="reference internal" href="modules/generated/sklearn.ensemble.AdaBoostRegressor.html#sklearn.ensemble.AdaBoostRegressor" title="sklearn.ensemble.AdaBoostRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.ensemble.AdaBoostRegressor</span></code></a></p></li>
</ul>
</section>