Back to GalleryNaive Bayes
Naive Bayes is a probabilistic classifier based on Bayes' Theorem. It makes the simplifying (naive) assumption that features are conditionally independent given the class label. It models the spatial coordinate distributions of each class to calculate posterior probabilities.
Key Parameters & Visual Influence:
• Distribution Type: Gaussian fits bell-shaped probability curves (generating smooth parabolic or elliptical boundaries), while Uniform assumes flat bounds.
• Laplace Smoothing: Adds a small constant to frequency counts, preventing zero probabilities for unseen coordinates and stabilizing decision regions.
Dataset Studio
CSV format: Headers in first row, last column = label/target. All values must be numeric.
Example: x1, x2, label