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Master H-Statistic: Uncover & Visualize Feature Interactions

IntermediateGuided Project

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.

Language

  • English

Topic

  • Machine Learning

Skills You Will Learn

  • Machine Learning, Explainable AI, Data Science, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • June 3, 2025
About this Guided Project
Imagine analyzing bike rental data and discovering that your model predicts accurately on sunny days but fails completely during rainy rush hours. Traditional feature importance methods might tell you that both "weather" and "time" matter, but they miss how these factors work together. This is the power of understanding feature interactions—the collaborative effects that create patterns beyond what individual features reveal.

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

This lab teaches you to detect and measure feature interactions through a comprehensive workflow:
1️⃣ Synthetic Data Exploration - Generate datasets with controlled interaction effects to understand how the H-statistic detects relationship patterns
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

By completing this lab, you will:
  • 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

This project is ideal for:
  • 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
No advanced statistical expertise required—basic machine learning knowledge and curiosity about model interpretation are all you need.

What You Need

✅ A browser to access the lab environment
✅ 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.

Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Karan Goswami

Data Scientist

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

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Contributors

Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

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