Package index • OptimalBinningWoE

All functions

bake(<step_obwoe>): Apply the Optimal Binning Transformation
control.obwoe(): Control Parameters for Optimal Binning Algorithms
.categorical_only_algorithms(): Categorical-Only Algorithms
.numerical_only_algorithms(): Numerical-Only Algorithms
.universal_algorithms(): Universal Algorithms
.valid_algorithms(): Valid Binning Algorithms
fit_logistic_regression(): Fit Logistic Regression Model
ob_apply_woe_cat(): Apply Optimal Weight of Evidence (WoE) to a Categorical Feature
ob_apply_woe_num(): Apply Optimal Weight of Evidence (WoE) to a Numerical Feature
ob_categorical_cm(): Optimal Binning for Categorical Variables using Enhanced ChiMerge Algorithm
ob_categorical_dmiv(): Optimal Binning for Categorical Variables using Divergence Measures
ob_categorical_dp(): Optimal Binning for Categorical Variables using Dynamic Programming
ob_categorical_fetb(): Optimal Binning for Categorical Variables using Fisher's Exact Test
ob_categorical_gmb(): Optimal Binning for Categorical Variables using Greedy Merge Algorithm
ob_categorical_ivb(): Optimal Binning for Categorical Variables using Information Value Dynamic Programming
ob_categorical_jedi(): Optimal Binning for Categorical Variables using JEDI Algorithm
ob_categorical_jedi_mwoe(): Optimal Binning for Categorical Variables with Multinomial Target using JEDI-MWoE
ob_categorical_mba(): Optimal Binning for Categorical Variables using Monotonic Binning Algorithm
ob_categorical_milp(): Optimal Binning for Categorical Variables using Heuristic Algorithm
ob_categorical_mob(): Optimal Binning for Categorical Variables using Monotonic Optimal Binning (MOB)
ob_categorical_sab(): Optimal Binning for Categorical Variables using Simulated Annealing
ob_categorical_sblp(): Optimal Binning for Categorical Variables using SBLP
ob_categorical_sketch(): Optimal Binning for Categorical Variables using Sketch-based Algorithm
ob_categorical_swb(): Optimal Binning for Categorical Variables using Sliding Window Binning (SWB)
ob_categorical_udt(): Optimal Binning for Categorical Variables using a User-Defined Technique (UDT)
ob_cutpoints_cat(): Binning Categorical Variables using Custom Cutpoints
ob_cutpoints_num(): Binning Numerical Variables using Custom Cutpoints
ob_gains_table(): Compute Comprehensive Gains Table from Binning Results
ob_gains_table_feature(): Compute Gains Table for a Binned Feature Vector
ob_numerical_bb(): Optimal Binning for Numerical Variables using Branch and Bound Algorithm
ob_numerical_cm(): Optimal Binning for Numerical Variables using Enhanced ChiMerge Algorithm
ob_numerical_dmiv(): Optimal Binning using Metric Divergence Measures (Zeng, 2013)
ob_numerical_dp(): Optimal Binning for Numerical Variables using Dynamic Programming
ob_numerical_ewb(): Hybrid Optimal Binning using Equal-Width Initialization and IV Optimization
ob_numerical_fast_mdlp(): Optimal Binning using MDLP with Monotonicity Constraints
ob_numerical_fetb(): Optimal Binning using Fisher's Exact Test
ob_numerical_ir(): Optimal Binning using Isotonic Regression (PAVA)
ob_numerical_jedi(): Optimal Binning using Joint Entropy-Driven Interval Discretization (JEDI)
ob_numerical_jedi_mwoe(): Optimal Binning for Multiclass Targets using JEDI M-WOE
ob_numerical_kmb(): Optimal Binning using K-means Inspired Initialization (KMB)
ob_numerical_ldb(): Optimal Binning for Numerical Variables using Local Density Binning
ob_numerical_lpdb(): Optimal Binning using Local Polynomial Density Binning (LPDB)
ob_numerical_mblp(): Optimal Binning for Numerical Features Using Monotonic Binning via Linear Programming
ob_numerical_mdlp(): Optimal Binning for Numerical Features using Minimum Description Length Principle
ob_numerical_mob(): Optimal Binning for Numerical Features using Monotonic Optimal Binning
ob_numerical_mrblp(): Optimal Binning for Numerical Features using Monotonic Risk Binning with Likelihood Ratio Pre-binning
ob_numerical_oslp(): Optimal Binning for Numerical Variables using Optimal Supervised Learning Partitioning
ob_numerical_sketch(): Optimal Binning for Numerical Variables using Sketch-based Algorithm
ob_numerical_ubsd(): Optimal Binning for Numerical Variables using Unsupervised Binning with Standard Deviation
ob_numerical_udt(): Optimal Binning for Numerical Variables using Entropy-Based Partitioning
ob_preprocess(): Data Preprocessor for Optimal Binning
obcorr(): Compute Multiple Robust Correlations Between Numeric Variables
obwoe(): Unified Optimal Binning and Weight of Evidence Transformation
obwoe_algorithm(): Binning Algorithm Parameter
obwoe_algorithms(): List Available Algorithms
obwoe_apply(): Apply Weight of Evidence Transformations to New Data
obwoe_bin_cutoff(): Bin Cutoff Parameter
obwoe_gains(): Gains Table Statistics for Credit Risk Scorecard Evaluation
obwoe_max_bins(): Maximum Bins Parameter
obwoe_min_bins(): Minimum Bins Parameter
plot(<obwoe>): Plot Method for obwoe Objects
plot(<obwoe_gains>): Plot Gains Table
prep(<step_obwoe>): Prepare the Optimal Binning Step
print(<obwoe>): Print Method for obwoe Objects
print(<step_obwoe>): Print Method for step_obwoe
required_pkgs(<step_obwoe>): Required Packages for step_obwoe
step_obwoe(): Optimal Binning and WoE Transformation Step
summary(<obwoe>): Summary Method for obwoe Objects
tidy(<step_obwoe>): Tidy Method for step_obwoe
tunable(<step_obwoe>): Tunable Parameters for step_obwoe