Model Explanation

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Out:

Running DummyClassifier(strategy='prior')
accuracy: 0.391 recall_macro: 0.333 precision_macro: 0.130 f1_macro: 0.187
=== new best DummyClassifier(strategy='prior') (using recall_macro):
accuracy: 0.391 recall_macro: 0.333 precision_macro: 0.130 f1_macro: 0.187

Running GaussianNB()
accuracy: 0.970 recall_macro: 0.972 precision_macro: 0.972 f1_macro: 0.970
=== new best GaussianNB() (using recall_macro):
accuracy: 0.970 recall_macro: 0.972 precision_macro: 0.972 f1_macro: 0.970

Running MultinomialNB()
accuracy: 0.932 recall_macro: 0.935 precision_macro: 0.942 f1_macro: 0.936
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.557 recall_macro: 0.602 precision_macro: 0.417 f1_macro: 0.473
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.872 recall_macro: 0.862 precision_macro: 0.886 f1_macro: 0.866
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.872 recall_macro: 0.862 precision_macro: 0.886 f1_macro: 0.866
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.962 recall_macro: 0.967 precision_macro: 0.967 f1_macro: 0.961
Running LogisticRegression(class_weight='balanced', max_iter=1000)
accuracy: 0.969 recall_macro: 0.973 precision_macro: 0.971 f1_macro: 0.969
=== new best LogisticRegression(class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.969 recall_macro: 0.973 precision_macro: 0.971 f1_macro: 0.969


Best model:
LogisticRegression(class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.969 recall_macro: 0.973 precision_macro: 0.971 f1_macro: 0.969
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        13
           1       0.95      1.00      0.97        19
           2       1.00      0.92      0.96        13

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

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/home/circleci/project/dabl/plot/utils.py:375: 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)