Model Selection¶

Model Validation¶

`cross_validate`(estimator, input_relation, X, y)	Computes the K-Fold cross validation of an estimator.
`learning_curve`(estimator, input_relation, X, y)	Draws the learning curve.

Variable Selection¶

`randomized_features_search_cv`(estimator, ...)	Computes the k-fold grid search of an estimator using different feature combinations.
`stepwise`(estimator, input_relation, X, y[, ...])	Uses the Stepwise algorithm to find the most suitable number of features when fitting the estimator.

Hyper Parameter Tuning¶

`gen_params_grid`(estimator[, nbins, ...])	Generates the estimator grid.
`grid_search_cv`(estimator, param_grid, ...[, ...])	Computes the k-fold grid search of an estimator.
`parameter_grid`(param_grid)	Generates a `list` of the different combinations of input parameters.
`plot_acf_pacf`(vdf, column, ts[, by, p, show])	Draws the ACF and PACF Charts.
`randomized_search_cv`(estimator, ...[, ...])	Computes the K-Fold randomized search of an estimator.
`validation_curve`(estimator, param_name, ...)	Draws the validation curve.

K-Means¶

`best_k`(input_relation[, X, n_clusters, ...])	Finds the `KMeans` `k` based on a score.
`elbow`(input_relation[, X, n_clusters, init, ...])	Draws an Elbow curve.

Statistical Tests¶

See Statistical Functions.