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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.

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Guided 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.

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.

Estimated Effort

45 Minutes

Level

Intermediate

Skills You Will Learn

Data Science, Explainable AI, Machine Learning, Python

Language

English

Course Code

GPXX0C5AEN

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