At a Glance

Name: Exploring Feature Interactions using H-Statistic
Price: Free CAD
Rating: 5.0 (1 reviews)
Author: joseph_santarcangelo, karan_goswami, faranak_heidari

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.

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.

Offered By: IBMSkillsNetwork

Exploring Feature Interactions using H-Statistic

At a Glance

Project Overview

What You'll Learn

Who Should Do This Lab

What You Need