Machine Learning, Regression, Data Science.
variance). To avoid overfitting, you can use cross-validation and learning curves. from sklearn.pipeline import make_pipeline poly_model = make_pipeline(PolynomialFeatures(7),LinearRegression()) poly_model.fit(x_train, y_train) y_pred = poly_model.predict(x_test) print(poly_model.intercept_,