APLR documentation (#658)

* reducing aplr chart resolution to reduce documentation size

Signed-off-by: Mathias Ottenbreit <mathiasottenbreit@gmail.com>

* documentation size reduction for aplr

Signed-off-by: Mathias Ottenbreit <mathiasottenbreit@gmail.com>

* documentation size reduction for aplr

Signed-off-by: Mathias Ottenbreit <mathiasottenbreit@gmail.com>

* documentation size reduction for aplr

Signed-off-by: Mathias Ottenbreit <mathiasottenbreit@gmail.com>

---------

Signed-off-by: Mathias Ottenbreit <mathiasottenbreit@gmail.com>

Author: mathias-von-ottenbreit

docs/interpret/aplr.ipynb

@@ -59,7 +59,7 @@
     "    X, y, test_size=0.20, random_state=seed\n",
     ")\n",
     "\n",
-    "aplr = APLRClassifier(random_state=seed)\n",
+    "aplr = APLRClassifier(random_state=seed, max_interaction_level=0)\n",
     "aplr.fit(X_train, y_train, X_names=X_train.columns)\n",
     "\n",
     "auc = roc_auc_score(y_test, aplr.predict_class_probabilities(X_test)[:, 1])\n",
@@ -93,7 +93,7 @@
    "source": [
     "<h2>Further Resources</h2>\n",
     "\n",
-    "Below are more detailed code examples from the backing repository for APLR. This will also work when using APLR through `InterpretML` if you change `from aplr import` to `from interpret.glassbox import`. For better precision in global explanation plots you may consider using the plotting functionality in native APLR, introduced in APLR version 10.13.0.\n",
+    "Below are more detailed code examples from the backing repository for APLR. This will also work when using APLR through `InterpretML` if you change `from aplr import` to `from interpret.glassbox import`. Larger models or those with complex interactions may load slowly in `explain_global()` or `explain_local()`. For these cases, or for better precision in global explanation plots, we recommend using the native plotting functionality introduced in APLR v10.13.0.\n",
     "- [Regression code example](https://github.com/ottenbreit-data-science/aplr/blob/main/examples/train_aplr_regression.py)\n",
     "- [Classification code example](https://github.com/ottenbreit-data-science/aplr/blob/main/examples/train_aplr_classification.py)"
    ]