Function reference • forester

All functions
`adult`	Adult dataset
`basic_info()`	Provide basic dataset information
`best_model_predict()`	Predict a single model
`binarize_target()`	Binarize the target column
`boruta_selection()`	Perform Boruta algorithm for selecting most important features
`check_cor()`	Search for strongly correlated values (Spearman for numerical, Crammer V for categorical)
`check_data()`	Run data check pipeline to seek for potential problems with the data
`check_dim()`	Search for dimensionality problems in the dataset
`check_duplicate_col()`	Search for duplicates between columns
`check_missing()`	Search for missing values in the target column and predictors
`check_outliers()`	Search for outliers via mean standard deviation, median absolute deviation and inter quantile range
`check_static()`	Search for columns dominated by a single value
`check_y_balance()`	Check whether the target column is unbalanced (for regression it bins values via quantiles)
`choose_best_models()`	Choose the bests models, according to the score data frame
`compas`	Modified COMPAS dataset
`create_ranked_list()`	Create final ranked_list
`delete_correlated_values()`	Delete correlated values
`delete_id_columns()`	Delete columns that are ID-like columns
`detect_id_columns()`	Detect columns that are ID-like columns
`draw_boxplot()`	Draw boxplot of resuduals - for regression
`draw_confusion_matrix()`	Draw confusion matrix for the model
`draw_feature_importance()`	Draw Feature Importance plot
`draw_radar_plot()`	Plot radar chart of one metric
`draw_rmse_plot()`	Draws train vs test RMSE plot for models
`draw_roc_plot()`	Draw AUC ROC curve for the best model
`draw_scatterplot()`	Draw scatterplot of true vs predicted values of target for training and test data for one model
`explain()`	Explain forester model
`fertility`	Fertility dataset
`forester_palette()`	Return colors from palette
`format_models_details()`	Format info about models
`guess_type()`	Guess task type by the target value from the dataset
`lisbon`	Lisbon dataset
`lymph`	Lymph dataset
`manage_missing()`	Manage missing values
`predict_models()`	Predict models depending on the engine
`predict_models_all()`	Predictions for a list of models with multiple occurrences of the same types of models
`predict_new()`	Perform predictions on new data
`prepare_data()`	Prepare data into format correct for the selected model engine
`preprocessing()`	Conduct preprocessing processes
`pre_rm_static_cols()`	Remove columns with one value for all rows
`random_search()`	Random optimization of hyperparameters
`report()`	Generate report after training
`save()`	Save elements from forester
`save_deleted_columns()`	Save column names deleted during preprocessing process
`score_models()`	Score models by suitable metrics
`testing_data`	Testing dataset
`train()`	Train models with forester
`train_models()`	Train models from given engines
`train_models_bayesopt()`	Train models with Bayesian Optimization algorithm
`train_test_balance()`	Balance and split the dataset
`verbose_cat()`	Print the provided cat-like input if verbose is TRUE

Reference

All functions