from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split def plot_learning_curves(model, X, y): X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2) train_errors, val_errors = [], [] for m in range(1, len(X_train)): model.fit(X_train[:m], y_train[:m]) y_train_predict = model.predict(X_train[:m]) y_val_predict = model.predict(X_val) train_errors.append(mean_squared_error(y_train[:m], y_train_predict)) val_errors.append(mean_squared_error(y_val, y_val_predict)) plt.plot(np.sqrt(train_errors), "r-+", linewidth=2, label="train") plt.plot(np.sqrt(val_errors), "b-", linewidth=3, label="val")