DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True)

[MilSix]

Not sure how, but I got DataConversionWarning at this the following codes cell: (1)

svm_confirmed = svm_search.bestestimator

svm_confirmed = SVR(shrinking=True, kernel='poly',gamma=0.01, epsilon=1,degree=3, C=0.1) svm_confirmed.fit(X_train_confirmed, y_train_confirmed) svm_pred = svm_confirmed.predict(future_forcast)

output(1) /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/utils/validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel(). y = column_or_1d(y, warn=True) (2):

bayesian ridge polynomial regression

tol = [1e-6, 1e-5, 1e-4, 1e-3, 1e-2] alpha_1 = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3] alpha_2 = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3] lambda_1 = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3] lambda_2 = [1e-7, 1e-6, 1e-5, 1e-4, 1e-3] normalize = [True, False]

bayesian_grid = {'tol': tol, 'alpha_1': alpha_1, 'alpha_2' : alpha_2, 'lambda_1': lambda_1, 'lambda_2' : lambda_2, 'normalize' : normalize}

bayesian = BayesianRidge(fit_intercept=False) bayesian_search = RandomizedSearchCV(bayesian, bayesian_grid, scoring='neg_mean_squared_error', cv=3, return_train_score=True, n_jobs=-1, n_iter=40, verbose=1) bayesian_search.fit(bayesian_poly_X_train_confirmed, y_train_confirmed)

[MilSix]

clicked the wrong button. On bayesian ridge polynomial regression I got seems like infinite loop of warning(1). After I got about (x_train) warning it did display the following output: RandomizedSearchCV(cv=3, error_score='raise-deprecating', estimator=BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False, copy_X=True, fit_intercept=False, lambda_1=1e-06, lambda_2=1e-06, n_iter=300, normalize=False, tol=0.001, verbose=False), fit_params=None, iid='warn', n_iter=40, n_jobs=-1, param_distributions={'tol': [1e-06, 1e-05, 0.0001, 0.001, 0.01], 'alpha_1': [1e-07, 1e-06, 1e-05, 0.0001, 0.001], 'alpha_2': [1e-07, 1e-06, 1e-05, 0.0001, 0.001], 'lambda_1': [1e-07, 1e-06, 1e-05, 0.0001, 0.001], 'lambda_2': [1e-07, 1e-06, 1e-05, 0.0001, 0.001], 'normalize': [True, False]}, pre_dispatch='2*n_jobs', random_state=None, refit=True, return_train_score=True, scoring='neg_mean_squared_error', verbose=1)

[therealcyberlord]

Thanks for your feedback. Does the code work beside the warnings?

[MilSix]

yes it does. Took me a long while to skip through the warning.... 1st I thought I'll be in for infinite loop, luckily it did stop warning I guess when it reached the end of ast value of x_test.

[therealcyberlord]

Insert this snippet of code to ignore warnings

import warnings warnings.filterwarnings("ignore")

Random grid search cv essentially tries random combinations of different hyperparameters, which explains the loop of warning messages you see.

[abramosrotex]

Insert this snippet of code to ignore warnings

import warnings warnings.filterwarnings("ignore")

Random grid search cv essentially tries random combinations of different hyperparameters, which explains the loop of warning messages you see.

Thanks very much for this, it was very useful for me, as I have been looking for ways to remove the warnings.