Model Explanation

Running DummyClassifier()
accuracy: 0.376 recall_macro: 0.333 precision_macro: 0.125 f1_macro: 0.182
=== new best DummyClassifier() (using recall_macro):
accuracy: 0.376 recall_macro: 0.333 precision_macro: 0.125 f1_macro: 0.182

Running GaussianNB()
accuracy: 0.970 recall_macro: 0.972 precision_macro: 0.973 f1_macro: 0.971
=== new best GaussianNB() (using recall_macro):
accuracy: 0.970 recall_macro: 0.972 precision_macro: 0.973 f1_macro: 0.971

Running MultinomialNB()
accuracy: 0.963 recall_macro: 0.964 precision_macro: 0.967 f1_macro: 0.965
Running DecisionTreeClassifier(class_weight='balanced', max_depth=1)
accuracy: 0.571 recall_macro: 0.600 precision_macro: 0.417 f1_macro: 0.477
Running DecisionTreeClassifier(class_weight='balanced', max_depth=5)
accuracy: 0.925 recall_macro: 0.923 precision_macro: 0.940 f1_macro: 0.926
Running DecisionTreeClassifier(class_weight='balanced', min_impurity_decrease=0.01)
accuracy: 0.902 recall_macro: 0.902 precision_macro: 0.917 f1_macro: 0.905
Running LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)
accuracy: 0.985 recall_macro: 0.987 precision_macro: 0.987 f1_macro: 0.986
=== new best LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 0.985 recall_macro: 0.987 precision_macro: 0.987 f1_macro: 0.986

Running LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
accuracy: 1.000 recall_macro: 1.000 precision_macro: 1.000 f1_macro: 1.000
=== new best LogisticRegression(C=1, class_weight='balanced', max_iter=1000) (using recall_macro):
accuracy: 1.000 recall_macro: 1.000 precision_macro: 1.000 f1_macro: 1.000


Best model:
LogisticRegression(C=1, class_weight='balanced', max_iter=1000)
Best Scores:
accuracy: 1.000 recall_macro: 1.000 precision_macro: 1.000 f1_macro: 1.000
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        14
           1       1.00      0.95      0.98        21
           2       0.91      1.00      0.95        10

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

[[14  0  0]
 [ 0 20  1]
 [ 0  0 10]]
/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)