Offered By: IBMSkillsNetwork
Master H-Statistic: Uncover & Visualize Feature Interactions
Uncover hidden relationships that traditional feature importance tools miss by learning to analyze feature interactions using the H-statistic. In this hands-on project, you'll visualize joint effects with PDP and ICE plots, and apply interaction analysis to real-world bike sharing data. Measure interaction strength in decision trees and random forests, interpret pairwise and one-vs-all H-statistics, and compare additive vs. interactive model behavior. Gain practical skills to enhance model interpretability and guide feature engineering. Build skills that boost both model insight & performance.
Continue readingGuided Project
Machine Learning
At a Glance
Uncover hidden relationships that traditional feature importance tools miss by learning to analyze feature interactions using the H-statistic. In this hands-on project, you'll visualize joint effects with PDP and ICE plots, and apply interaction analysis to real-world bike sharing data. Measure interaction strength in decision trees and random forests, interpret pairwise and one-vs-all H-statistics, and compare additive vs. interactive model behavior. Gain practical skills to enhance model interpretability and guide feature engineering. Build skills that boost both model insight & performance.
In this hands-on lab, you'll master Friedman's H-statistic, a powerful technique for quantifying and visualizing how features collaborate in your model's decision-making process.
Project Overview
2️⃣ Visualization Techniques - Create PDP and ICE plots to visually identify where features influence each other
3️⃣ Interaction Quantification - Calculate pairwise H-statistics to measure exactly how strongly features collaborate
4️⃣ Real-World Application - Apply these techniques to the UCI Bike Sharing dataset to uncover meaningful interactions
By implementing the H-statistic methodology, you'll develop a deeper understanding of model behavior and reveal insights that traditional feature importance measures miss completely.
What You'll Learn
- Generate controlled datasets to understand how interactions manifest in data
- Build tree-based models and analyze their interaction capabilities
- Visualize feature relationships using Partial Dependence and ICE plots
- Calculate and interpret H-statistics to quantify interaction strength
- Identify which feature pairs most strongly influence predictions
- Apply your knowledge to enhance model interpretability in real-world data
Who Should Do This Lab
- Data scientists looking to go beyond basic feature importance analysis
- ML practitioners seeking deeper model interpretability
- Analysts who want to explain "why" models make certain predictions
- Anyone interested in advanced feature engineering techniques
What You Need
✅ Basic Python knowledge (understanding functions and data structures)
✅ Familiarity with machine learning concepts (decision trees, feature importance)
By the end of this project, you'll have mastered a powerful technique that transforms how you interpret machine learning models—enabling you to uncover hidden patterns that drive predictions and build more accurate models through informed feature engineering.
Estimated Effort
45 Minutes
Level
Intermediate
Skills You Will Learn
Data Science, Explainable AI, Machine Learning, Python
Language
English
Course Code
GPXX0C5AEN