How can we improve the performance of naive Bayes classifier?

How can we improve the performance of naive Bayes classifier?

Better Naive Bayes: 12 Tips To Get The Most From The Naive Bayes Algorithm

1. Missing Data. Naive Bayes can handle missing data.
2. Use Log Probabilities.
3. Use Other Distributions.
4. Use Probabilities For Feature Selection.
5. Segment The Data.
6. Re-compute Probabilities.
7. Use as a Generative Model.
8. Remove Redundant Features.

How good is the performance of naive Bayes model?

Pros: It is easy and fast to predict class of test data set. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data. It perform well in case of categorical input variables compared to numerical variable(s).

How to improve the naive Bayes classification performance?

The Naive Bayes classifier model performance can be calculated by the hold-out method or cross-validation depending on the dataset. We can evaluate the model performance with a suitable metric. In this section, we present some methods to increase the Naive Bayes classifier model performance:

When does naive Bayes reach a wrong conclusion?

If two features encode the same information, the Naive Bayes classifier will double-count their effect and reach a wrong conclusion. Another implicit assumption comes with the Naive Bayes classifier. The algorithm doesn’t treat features differently. So, all features have an equal effect on the result.

Why do we use conditional independence in naive Bayes?

The validity of the formula we use to compute the class probabilities relies on the conditional independence assumption. If two features encode the same information, the Naive Bayes classifier will double-count their effect and reach a wrong conclusion. Another implicit assumption comes with the Naive Bayes classifier.

How to improve naive Bayes for grid search?

By referencing the sklearn.naive_bayes.GaussianNB documentation, you can find a completed list of parameters with descriptions that can be used in grid search functionalities.