Welcome to kuplift’s documentation!¶
Description¶
It’s a User Parameter-free Bayesian Framework for Uplift Modeling.
How to use kuplift ?¶
- Documentation
- kuplift.bayesian_decision_tree module
- kuplift.bayesian_random_forest module
- kuplift.feature_selection module
- kuplift.univariate_encoding module
- kuplift.optimized_univariate_encoding module
OptimizedUnivariateEncodingOptimizedUnivariateEncoding.fit()OptimizedUnivariateEncoding.fit_transform()OptimizedUnivariateEncoding.get_level()OptimizedUnivariateEncoding.get_levels()OptimizedUnivariateEncoding.get_partition()OptimizedUnivariateEncoding.get_partitions()OptimizedUnivariateEncoding.get_target_frequencies()OptimizedUnivariateEncoding.get_target_probabilities()OptimizedUnivariateEncoding.get_treatment_groups()OptimizedUnivariateEncoding.get_uplift()OptimizedUnivariateEncoding.get_variable_type()OptimizedUnivariateEncoding.get_variable_types()OptimizedUnivariateEncoding.informative_input_variablesOptimizedUnivariateEncoding.input_variablesOptimizedUnivariateEncoding.noninformative_input_variablesOptimizedUnivariateEncoding.target_modalitiesOptimizedUnivariateEncoding.target_nameOptimizedUnivariateEncoding.target_treatment_pairsOptimizedUnivariateEncoding.transform()OptimizedUnivariateEncoding.treatment_modalitiesOptimizedUnivariateEncoding.treatment_name
- kuplift.mt_univariate_encoding module
- kuplift.mt_decision_tree module
MultiTreatmentDecisionTreeMultiTreatmentDecisionTree.fit()MultiTreatmentDecisionTree.get_leaf_paths()MultiTreatmentDecisionTree.get_node_by_id()MultiTreatmentDecisionTree.get_node_path_str()MultiTreatmentDecisionTree.get_target_frequencies()MultiTreatmentDecisionTree.get_target_probabilities()MultiTreatmentDecisionTree.get_treatment_groups_of_leaves()MultiTreatmentDecisionTree.get_uplift()MultiTreatmentDecisionTree.internal_nodesMultiTreatmentDecisionTree.leaf_ids_sorted_dfs()MultiTreatmentDecisionTree.leaf_nodesMultiTreatmentDecisionTree.node_ids_sorted_dfs()MultiTreatmentDecisionTree.node_ids_sorted_dfs_from()MultiTreatmentDecisionTree.predict_best_treatment()MultiTreatmentDecisionTree.predict_leaf_id()MultiTreatmentDecisionTree.predict_probabilities()MultiTreatmentDecisionTree.print_tree()MultiTreatmentDecisionTree.root_nodeMultiTreatmentDecisionTree.target_modalitiesMultiTreatmentDecisionTree.treatment_modalitiesMultiTreatmentDecisionTree.treatment_modality_countMultiTreatmentDecisionTree.tree_to_dot()MultiTreatmentDecisionTree.tree_to_image()MultiTreatmentDecisionTree.tree_to_string()MultiTreatmentDecisionTree.used_variable_count
- kuplift.mt_random_forest module
A note on the terminology used in the source code¶
The source code uses short names such as i, j or t. Here are their definitions:
i: part (interval for a numerical variable or value group for a categorical variable);
j: target (outcome);
t: treatment;
g: group of treatments;
N: number of observations (frequency);
P: probability.
The source code also refers to tables, represented by pandas.DataFrame and named using these short names.
For example: N_ijt, P_ijg, Uplift_ig.
Explanation of the example names:
N_ijt: Each value in the table is a number of observations. One DataFrame row contains the values for one part (i). One DataFrame column contains the values for one target-treatment pair (jt).
P_ijg: Each value in the table is a probability. One DataFrame row contains the values for one part (i). One DataFrame column contains the values for one target-treatmentgroup pair (jg).
Uplift_ig: Each value in the table is an uplift. One DataFrame row contains the values for one part (i). One DataFrame column contains the values for one treatment group (g).