How to implement Naive Bayes from scratch with Python

TL;DR

Learn about and code a Naive Bayes classifier, achieving 96.5% accuracy with a Gaussian distribution model on a toy dataset.

Transcript

welcome to another video of the machine learning from scratch course presented by assembly ai in this series we implement popular machine learning algorithms using only built in python functions and numpy in this lesson we learn about naive bayes as always we start with a short theory section and then we jump to the code so let's get started so nai... Read More

Key Insights

• ❓ Naive Bayes simplifies calculations by assuming feature independence.
• 🏛️ Class conditional probabilities are modeled using Gaussian distributions.
• ✋ The classifier predicts by selecting the class with the highest posterior probability.
• 🏛️ Prior probabilities are calculated using the frequency of each class label.
• ❓ Implementing Naive Bayes involves calculating means, variances, and priors.
• 🏛️ Prediction in Naive Bayes involves calculating posterior probabilities for each class label.
• ✋ The classifier achieves high accuracy with proper implementation and parameters.

Summary & Key Takeaways

• Naive Bayes is a probabilistic classifier based on Bayes' theorem with a naive independence assumption between features.

• The classifier assumes features are mutually independent for prediction, using prior and class conditional probabilities.

• In Python, implementation involves calculating means, variances, and priors for each class, then predicting based on posterior probabilities.