How does Naive Bayes handle missing value?

How does Naive Bayes handle missing value?

Naive Bayes can handle missing data. Attributes are handled separately by the algorithm at both model construction time and prediction time. As such, if a data instance has a missing value for an attribute, it can be ignored while preparing the model, and ignored when a probability is calculated for a class value.

How can Naive Bayes solve zero frequency problem?

The zero-frequency problem An approach to overcome this ‘zero-frequency problem’ in a Bayesian environment is to add one to the count for every attribute value-class combination when an attribute value doesn’t occur with every class value. This is how we’ll get rid of getting a zero probability.

How can Naive Bayes improve performance?

3. Ways to Improve Naive Bayes Classification Performance

1. 3.1. Remove Correlated Features.
2. 3.2. Use Log Probabilities.
3. 3.3. Eliminate the Zero Observations Problem.
4. 3.4. Handle Continuous Variables.
5. 3.5. Handle Text Data.
6. 3.6. Re-Train the Model.
7. 3.7. Parallelize Probability Calculations.
8. 3.8. Usage with Small Datasets.

How does the naive Bayes classifier handle missing data in training?

When classifying instances, the attribute with the missing value is simply not included in the probability calculation ( reference) In training, the instance [with the missing data] is not included in frequency count for attribute value-class combination. ( reference)

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 are feature vectors used in naive Bayes?

Multinomial Naive Bayes: Feature vectors represent the frequencies with which certain events have been generated by a multinomial distribution. This is the event model typically used for document classification. Bernoulli Naive Bayes: In the multivariate Bernoulli event model, features are independent booleans (binary variables) describing inputs.