Scaling, Normalization, Min Max.
KNeighborsClassifier())] pipeline = Pipeline(steps) parameters = {"knn__n_neighbors": range(1, 50)} #parameters = {knn__n_neighbors=np.arange(1, 50)} X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2, random_state=21) cv = GridSearchCV(pipeline, param_grid=parameters)