Model Explanation

Running DummyClassifier()
accuracy: 0.383 recall_macro: 0.333 precision_macro: 0.128 f1_macro: 0.185
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.383 recall_macro: 0.333 precision_macro: 0.128 f1_macro: 0.185

Running GaussianNB()
accuracy: 0.970 recall_macro: 0.973 precision_macro: 0.973 f1_macro: 0.971
=== new best GaussianNB() (using recall_macro):
accuracy: 0.970 recall_macro: 0.973 precision_macro: 0.973 f1_macro: 0.971

Running MultinomialNB()
accuracy: 0.932 recall_macro: 0.936 precision_macro: 0.943 f1_macro: 0.936
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.571 recall_macro: 0.609 precision_macro: 0.439 f1_macro: 0.486
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.932 recall_macro: 0.935 precision_macro: 0.936 f1_macro: 0.933
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.925 recall_macro: 0.931 precision_macro: 0.928 f1_macro: 0.926
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.993 recall_macro: 0.993 precision_macro: 0.994 f1_macro: 0.993
=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.993 recall_macro: 0.993 precision_macro: 0.994 f1_macro: 0.993

Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
accuracy: 0.993 recall_macro: 0.993 precision_macro: 0.994 f1_macro: 0.993

Best model:
LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.993 recall_macro: 0.993 precision_macro: 0.994 f1_macro: 0.993
              precision    recall  f1-score   support

           0       0.92      1.00      0.96        12
           1       0.95      0.90      0.92        20
           2       0.92      0.92      0.92        13

    accuracy                           0.93        45
   macro avg       0.93      0.94      0.94        45
weighted avg       0.93      0.93      0.93        45

[[12  0  0]
 [ 1 18  1]
 [ 0  1 12]]
/home/circleci/project/dabl/explain.py:45: UserWarning: Can't plot roc curve, install sklearn 0.22-dev
  warn("Can't plot roc curve, install sklearn 0.22-dev")

from dabl.models import SimpleClassifier
from dabl.explain import explain
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split

wine = load_wine()

X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target)

sc = SimpleClassifier()

sc.fit(X_train, y_train)

explain(sc, X_test, y_test)