Naïve Bayes

Naïve Bayes

Naïve Bayes

Description

Naïve Bayes is a family of classification algorithms based on the Bayes Theorem. All algorithms in the family share a common principle that the pair of features being classified are mutually independent.

Why to use

To classify text into possible categories.

When to use

When textual data needs to be classified.

When not to use

When the dataset does not contain textual data.

Prerequisites

  • The dataset should not contain any missing (NaN) values.
  • The dataset should contain at least one categorical and one textual variable.

Input

Textual Data

Output

Classified data with predicted labels

Statistical Methods used

  • Accuracy
  • Sensitivity
  • Specificity
  • F-score
  • Confusion Matrix

Limitations

It is difficult to get a set of independent predictors for the Naïve Bayes model



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