KS_bucket ( pred_proba, final_data, bucket = 10, method = 'quantile' )Ĭard = toad. transform ( data_selected ], labels = True ), x = col, target = 'target' ) export ()) # Visualisation to check binning results col = 'feature_name' bin_plot ( c. drop ( to_drop, axis = 1 ), y = 'target', method = 'chi', min_samples = 0.05 ) print ( c. Reliable fine binning with visualisation.stepwise ( data_woe, target = 'target', estimator = 'ols', direction = 'both', criterion = 'aic', exclude = to_drop ) select ( data, target = 'target', empty = 0.5, iv = 0.02, corr = 0.7, return_drop = True, exclude = ) final_data = toad. and stepwise feature selection (with optimised algorithm).Preliminary selection based on criteria.quality ( data, 'target', iv_only = True ) Simple IV calculation for all features.The following showcases some of the most popular features of toad, for more detailed demonstrations and user guidance, please refer to the tutorials. Its key functionality streamlines the most critical and time-consuming process such as feature selection and fine binning. to results validation and scorecard transformation. It provides intuitive functions of the entire process, from EDA, feature engineering and selection etc.

Toad is dedicated to facilitating model development process, especially for a scorecard.

Toad download free version