Model Explanation

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Out:

Running DummyClassifier()
accuracy: 0.406 recall_macro: 0.333 precision_macro: 0.135 f1_macro: 0.193
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.406 recall_macro: 0.333 precision_macro: 0.135 f1_macro: 0.193

Running GaussianNB()
accuracy: 0.954 recall_macro: 0.958 precision_macro: 0.956 f1_macro: 0.956
=== new best GaussianNB() (using recall_macro):
accuracy: 0.954 recall_macro: 0.958 precision_macro: 0.956 f1_macro: 0.956

Running MultinomialNB()
accuracy: 0.940 recall_macro: 0.937 precision_macro: 0.954 f1_macro: 0.941
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.579 recall_macro: 0.610 precision_macro: 0.441 f1_macro: 0.487
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.903 recall_macro: 0.903 precision_macro: 0.911 f1_macro: 0.902
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.888 recall_macro: 0.889 precision_macro: 0.895 f1_macro: 0.887
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.985 recall_macro: 0.987 precision_macro: 0.985 f1_macro: 0.985
=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.985 recall_macro: 0.987 precision_macro: 0.985 f1_macro: 0.985

Running LogisticRegression(class_weight='balanced', max_iter=1000)
accuracy: 0.985 recall_macro: 0.987 precision_macro: 0.985 f1_macro: 0.985

Best model:
LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 0.985 recall_macro: 0.987 precision_macro: 0.985 f1_macro: 0.985
              precision    recall  f1-score   support

           0       0.95      1.00      0.97        18
           1       1.00      0.88      0.94        17
           2       0.91      1.00      0.95        10

    accuracy                           0.96        45
   macro avg       0.95      0.96      0.95        45
weighted avg       0.96      0.96      0.95        45

[[18  0  0]
 [ 1 15  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)