from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import roc_auc_score

model = DecisionTreeClassifier(max_depth=5, min_samples_leaf=15)
model.fit(x_train, y_train)

y_pred = model.predict_proba(x_train)[:, 1]
train_auc = roc_auc_score(y_train, y_pred)
print('train_auc:', train_auc)
# 0.90

y_pred = model.predict_proba(x_val)[:, 1]
val_auc = roc_auc_score(y_val, y_pred)
print('val_auc:', val_auc)
# 0.87

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