Classification-and-Action-Rule-Extraction-using-Fragile-State-Index/RandomizableFilteredClassifier.txt at main · aarshg28/Classification-and-Action-Rule-Extraction-using-Fragile-State-Index · GitHub

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Randomizable Filter Classifier :

=== Run information ===

Scheme:       weka.classifiers.meta.RandomizableFilteredClassifier -F "weka.filters.unsupervised.attribute.RandomProjection -N 10 -R 42 -D Sparse1" -S 1 -W weka.classifiers.lazy.IBk -- -K 1 -W 0 -A "weka.core.neighboursearch.LinearNNSearch -A \"weka.core.EuclideanDistance -R first-last\""
Relation:     Group-14-Original-weka.filters.unsupervised.attribute.Discretize-B10-M-1.0-Rfirst-last-precision6
Instances:    178
Attributes:   17
              Country
              Year
              Rank
              Total
              C1: Security Apparatus
              C2: Factionalized Elites
              C3: Group Grievance
              E1: Economy
              E2: Economic Inequality
              E3: Human Flight and Brain Drain
              P1: State Legitimacy
              P2: Public Services
              P3: Human Rights
              S1: Demographic Pressures
              S2: Refugees and IDPs
              X1: External Intervention
              Fragile State Index
Test mode:    10-fold cross-validation

=== Classifier model (full training set) ===

RandomizableFilteredClassifier using weka.classifiers.lazy.IBk -K 1 -W 0 -A "weka.core.neighboursearch.LinearNNSearch -A \"weka.core.EuclideanDistance -R first-last\"" on data filtered through weka.filters.unsupervised.attribute.RandomProjection -N 10 -R -990970977 -D Sparse1

Filtered Header
@relation Group-14-Original-weka.filters.unsupervised.attribute.Discretize-B10-M-1.0-Rfirst-last-precision6-weka.filters.supervised.attribute.NominalToBinary-weka.filters.unsupervised.attribute.RandomProjection-N10-R-990970977-DSparse1

@attribute K1 numeric
@attribute K2 numeric
@attribute K3 numeric
@attribute K4 numeric
@attribute K5 numeric
@attribute K6 numeric
@attribute K7 numeric
@attribute K8 numeric
@attribute K9 numeric
@attribute K10 numeric
@attribute 'Fragile State Index' {Alert,Warning,Stable,Sustainable}

@data


Classifier Model
IB1 instance-based classifier
using 1 nearest neighbour(s) for classification


Time taken to build model: 0.06 seconds

=== Stratified cross-validation ===
=== Summary ===

Correctly Classified Instances         104               58.427  %
Incorrectly Classified Instances        74               41.573  %
Kappa statistic                          0.3753
Mean absolute error                      0.2119
Root mean squared error                  0.4505
Relative absolute error                 64.3474 %
Root relative squared error            111.1696 %
Total Number of Instances              178

=== Detailed Accuracy By Class ===

                 TP Rate  FP Rate  Precision  Recall   F-Measure  MCC      ROC Area  PRC Area  Class
                 0.629    0.168    0.478      0.629    0.543      0.418    0.709     0.371     Alert
                 0.663    0.292    0.694      0.663    0.678      0.371    0.672     0.624     Warning
                 0.385    0.108    0.500      0.385    0.435      0.306    0.639     0.323     Stable
                 0.533    0.055    0.471      0.533    0.500      0.452    0.719     0.292     Sustainable
Weighted Avg.    0.584    0.207    0.590      0.584    0.583      0.373    0.676     0.481

=== Confusion Matrix ===

  a  b  c  d   <-- classified as
 22  9  2  2 |  a = Alert
 17 59 11  2 |  b = Warning
  5 14 15  5 |  c = Stable
  2  3  2  8 |  d = Sustainable