Machine Learning KNIME Hands-On Series (Day 8) - PCA

💡In today’s session, we’ll explore PCA (Principal Component Analysis) — one of the most powerful feature extraction and dimensionality reduction techniques in Machine Learning.

You’ll learn how to:
✅ Perform PCA in KNIME — step-by-step with a real dataset (Miles per Gallon).
✅ Compare a Linear Regression model before and after PCA.
✅ Understand how PCA reduces dimensions while retaining key information.
✅ See how PCA impacts model performance (R² value) and helps prevent overfitting.

🔍 Key Concepts Covered:

What is PCA and why it’s used

Feature extraction and dimensionality reduction

Handling missing values and categorical data

Building regression models with and without PCA

Comparing results using Numeric Scorer in KNIME

💡 Key Takeaway:
PCA helps simplify complex datasets, reduce multicollinearity, improve model efficiency, and lower computational cost — all while maintaining accuracy!

📺 Watch more hands-on tutorials in the LearnNoCodeAI playlist:
👉 https://www.youtube.com/@InquisitiveMinds-AI

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Видео Machine Learning KNIME Hands-On Series (Day 8) - PCA канала InquisitiveMinds - AI