Optimal Binning and Weight of Evidence Framework for Modeling
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Documentation for package ‘OptimalBinningWoE’ version 1.0.3
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| .categorical_only_algorithms | Categorical-Only Algorithms |
| .numerical_only_algorithms | Numerical-Only Algorithms |
| .universal_algorithms | Universal Algorithms |
| .valid_algorithms | Valid Binning Algorithms |
| bake.step_obwoe | Apply the Optimal Binning Transformation |
| control.obwoe | Control Parameters for Optimal Binning Algorithms |
| fit_logistic_regression | Fit Logistic Regression Model |
| 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 |
| 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 |
| 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 |