Mastering Laplace Smoothing in Naive Bayes: Avoiding Overfitting
Laplace smoothing in Naive Bayes models is a key technique to prevent overfitting and improve model accuracy, especially when dealing with limited data. We’ll explore the necessity of smoothing, how Laplace smoothing works, and its benefits. Additionally, we’ll compare alternative techniques like Lidstone Smoothing, Good-Turing Smoothing, and Backoff Interpolation.
Whether you're building a spam filter or working with machine learning models, understanding Laplace smoothing will help you handle zero-probability issues and boost model performance. Tune in to gain practical insights and improve your machine learning skills.
🔑 Video Timestamps
0:00 - Introduction to Laplace Smoothing in Naive Bayes
0:55 - Why Smoothing is Necessary in Machine Learning
1:54 - Overfitting and Zero Probabilities Explained
4:08 - Laplace Smoothing in Spam Filtering
8:12 - Alternative Smoothing Techniques: Lidstone, Good-Turing, and Backoff
9:52 - Conclusion: Choosing the Right Smoothing Method
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