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Exploring Feature Interactions using H-Statistic

BeginnerGuided Project

Discover hidden relationships in machine learning models with H-statistic analysis, revealing how features work together beyond their individual effects. When buying a car, colour and engine type matter but it's the red sports car with a turbo engine that really sells. In this project, learn to quantify high impact feature combinations and interpret how they influence predictions. Train decision tree models, calculate pairwise and one-vs-all interaction metrics, and analyze which feature combinations (such as holiday×windspeed) most strongly affect bike rental patterns.

Language

  • English

Topic

  • Machine Learning

Skills You Will Learn

  • Machine Learning, Data Science, Explainable AI, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • June 3, 2025
About this Guided Project
Imagine understanding why your model makes predictions beyond simple feature importance. What if you could identify exactly how features collaborate to influence outcomes? This is the power of feature interaction analysis using the H-statistic.

In this hands-on lab, you'll uncover the hidden patterns in how variables work together to affect predictions—transforming how you interpret machine learning models and engineer more effective features.

Project Overview

This lab teaches you to quantify and visualize feature interactions in a real-world bike sharing prediction scenario:
1️⃣ Feature Interaction Fundamentals - Learn why combinations of features (like time of day × workday status) can create effects that individual features alone don't capture
2️⃣ H-Statistic Calculation - Master techniques for measuring exactly how strongly features interact, both in pairs and overall
3️⃣ Practical Application - Apply these techniques to discover which feature combinations most strongly influence bike rental patterns
4️⃣ Visualization & Interpretation - Translate statistical findings into actionable insights using partial dependence plots
By measuring interactions with the H-statistic calculator, you'll detect patterns like how wind affects bike rentals differently on holidays versus workdays—insights that traditional feature importance measures completely miss.

What You'll Learn

By completing this lab, you will:
  • Understand the mathematics behind the H-statistic and how it quantifies interaction strength
  • Calculate both pairwise (Hij) and one-vs-all (Hj) interaction statistics
  • Identify the strongest feature interactions in real-world data
  • Visualize how features jointly influence predictions using partial dependence plots
  • Apply insights to improve feature engineering and model interpretation

Who Should Do This Lab

This project is ideal for:
  • Data scientists seeking deeper insights from their models
  • ML engineers wanting to improve feature engineering through interaction analysis
  • Analysts needing to explain complex model behavior to stakeholders
  • AI enthusiasts interested in advanced model interpretation techniques
No advanced statistics expertise required—basic machine learning knowledge and curiosity about model behavior 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 for uncovering the collaborative effects of features—enabling you to build more accurate models and explain their behaviour with unprecedented clarity.

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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