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Model Selection#

Model Validation#

cross_validate(estimator, input_relation, X, y)

Computes the K-Fold cross

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

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#

bayesian_search_cv(estimator, ...[, metric, ...])

Computes the k-fold bayesian

enet_search_cv(input_relation, X, y[, ...])

Computes the k-fold grid

gen_params_grid(estimator[, nbins, ...])

Generates the estimator grid.

grid_search_cv(estimator, param_grid, ...[, ...])

Computes the k-fold grid

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

validation_curve(estimator, param_name, ...)

Draws the validation curve.

K-Means#

best_k(input_relation[, X, n_cluster, init, ...])

Finds the KMeans / KPrototypes k based on a score.

elbow(input_relation[, X, n_cluster, init, ...])

Draws an Elbow curve.

Statistical Tests#

See Statistical Functions.