Feature Engineering & Tree-Based Methods

Feature engineering techniques like Weight of Evidence (WoE) and Information Value (IV) help transform and select predictors. Meanwhile, decision trees split data recursively for transparent classification. Together, they enable better model stability under correlated or categorical variables and yield intuitive rule-based structures.

WEIGHT OF EVIDENCE (WoE) & INFORMATION VALUE (IV)

Learning Objectives

• Compute WoE for binned variables to handle correlated categories

• Interpret IV thresholds for predictive strength

• Incorporate WoE-coded variables in classification models

Indicative Content

• WoE Calculation

  • log ratio of good vs. bad distributions

• IV Formula

  • Summation of WoE × difference in distributions

• Implementation

  • Custom or specialized Python scripts

DECISION TREES

Learning Objectives

• Split data recursively via Gini/entropy to create classification/regression trees

• Prune to avoid overfitting, interpret final leaf nodes

• Visualize with plot utilities or specialized tools

Indicative Content

• Algorithms

  • CART (Gini), ID3/C4.5 (entropy), CHAID

• Stopping & Pruning

  • max_depth, min_samples_split, cost complexity

• Implementation

  • DecisionTreeClassifier, DecisionTreeRegressor

TOOLS & METHODOLOGIES (ADVANCED FEATURE ENGINEERING & TREE-BASED METHODS)

• Python

  • Custom WoE/IV code, sklearn.tree.DecisionTreeClassifier

• Evaluation

  • Node interpretability, classification metrics (accuracy, precision/recall)

• Workflow

  • WoE transformation → decision tree modeling → pruning/tuning → final interpretability