Model Explanation

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Out:

Running DummyClassifier()
accuracy: 0.338 recall_macro: 0.333 precision_macro: 0.113 f1_macro: 0.169
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.338 recall_macro: 0.333 precision_macro: 0.113 f1_macro: 0.169

Running GaussianNB()
accuracy: 0.962 recall_macro: 0.964 precision_macro: 0.967 f1_macro: 0.964
=== new best GaussianNB() (using recall_macro):
accuracy: 0.962 recall_macro: 0.964 precision_macro: 0.967 f1_macro: 0.964

Running MultinomialNB()
accuracy: 0.954 recall_macro: 0.957 precision_macro: 0.959 f1_macro: 0.957
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.624 recall_macro: 0.650 precision_macro: 0.456 f1_macro: 0.525
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.872 recall_macro: 0.880 precision_macro: 0.883 f1_macro: 0.871
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.917 recall_macro: 0.920 precision_macro: 0.925 f1_macro: 0.918
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.977 recall_macro: 0.979 precision_macro: 0.978 f1_macro: 0.978
=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.977 recall_macro: 0.979 precision_macro: 0.978 f1_macro: 0.978

Running LogisticRegression(class_weight='balanced', max_iter=1000)
accuracy: 0.970 recall_macro: 0.970 precision_macro: 0.972 f1_macro: 0.970

Best model:
LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.977 recall_macro: 0.979 precision_macro: 0.978 f1_macro: 0.978
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        12
           1       1.00      0.96      0.98        23
           2       0.91      1.00      0.95        10

    accuracy                           0.98        45
   macro avg       0.97      0.99      0.98        45
weighted avg       0.98      0.98      0.98        45

[[12  0  0]
 [ 0 22  1]
 [ 0  0 10]]
/home/circleci/project/dabl/plot/utils.py:378: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax.set_yticklabels(

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)