Machine Learning - KNIME Hands-On Series (Day 6) | Decision Trees

🚀 In this video, we explore Decision Trees using an e-commerce shopping behavior dataset. You’ll learn step by step how to:

Load and explore customer shopping data (pages visited, bounce rate, exit rate, region, browser, returning customer status, etc.)

Identify the target variable (Revenue)

Split the dataset into training and test sets with stratified sampling

Build a Decision Tree model using KNIME’s learner and predictor nodes

Understand the role of Gini Index in splitting nodes

Learn how pruning helps prevent overfitting and improves accuracy

Compare results with and without pruning (87% → 89.7% accuracy!)

🔑 Key Takeaways

Decision Trees use impurity measures (like Gini Index) to decide the best splits.

Pruning is crucial to avoid overfitting and make the model generalize better.

Always validate on test data, not just training accuracy.

📊 Dataset: E-commerce shopping behavior (customer visit patterns, page values, bounce & exit rates, visitor type, special days, etc.)

Whether you are new to KNIME or want to strengthen your ML foundations, this session will give you a practical understanding of how decision trees work.

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Видео Machine Learning - KNIME Hands-On Series (Day 6) | Decision Trees канала InquisitiveMinds - AI