text_plain_output_1.png
from sklearn.metrics import confusion_matrix,recall_score, accuracy_score , classification_report, balanced_accuracy_score

from sklearn.tree import DecisionTreeClassifier

y_train.value_counts(normalize=True)

from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(max_depth=20, criterion='entropy', random_state=42)
clf.fit(X_train, y_train)

from sklearn.metrics import confusion_matrix, recall_score, accuracy_score, classification_report, balanced_accuracy_score
def print_performance(text, y_pred):
    pass

y_pred = clf.predict(X_test)
print_performance('Single Decision Tree', y_pred)