from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss

X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)
model = DecisionTreeClassifier()
best_loss = np.inf

for epoch in range(100):
    model.fit(X_train, y_train)
    val_preds = model.predict(X_val)
    loss = log_loss(y_val, val_preds)
    if loss < best_loss:
        best_loss = loss
    else:
        break