1.2 Gaussian Naive Bayes classifiers
- use continuous or real-valued features
- During training, Classifier estimates the mean and standard deviation of each feature for each class.
How it predicts
- For prediction, the classifier compares the features of the new example data point with the feature statistics for each class and selects the class that best matches the data point.
Assumption
- The Gaussian Naive Bayes Classifier assumes that the data for each class is generated by a simple class-specific Gaussian distribution and pick the class that has the highest probability of generating this data point.
Decision boundary
- The decision boundary between classes in the two-class Gaussian Naive Bayes Classifier. In general, is a parabolic curve between classes.
- It will be linear when the variance of these features is the same for both classes.
Application
- no special parameters to control the models complexity.