Categorical Naive Bayes

Categorical Naive Bayes

Categorical Naive Bayes

Description

The categorical Naïve Bayes algorithm is suitable for categorically discrete values like Weather Prediction, and Medical Diagnosis. It is the simplest and fastest classification algorithm.

Why to use

It is the simplest and fastest algorithm for a large chunk of textual data.

When to use

When you want to process discrete features that are categorically distributed on a large amount of data.

When not to use

When your dataset does not contain a categorical dependent variable

Prerequisites

–


Input

A dataset with one categorical dependent variable.

Output

  • Confusion Matrix
  • ROC Chart
  • Lift Chart

Statistical Methods Used

  • Accuracy
  • F-score
  • Precision
  • Sensitivity/Recall
  • Specificity

Limitations

  • In case a category does not exist in the test dataset then the algorithm assigns zero frequency.
  • This method has a performance overhead.

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