Tuning a Naive Bayes Model

Naive Bayes calculates probabilities by dividing pairwise occurrence percentages by singleton occurrence percentages, improving model performance with threshold adjustments.

If these percentages are very small for a given predictor, they probably do not contribute to the effectiveness of the model. Occurrences below a certain threshold can usually be ignored.

The following build settings are available for adjusting the probability thresholds. You can specify:

• The minimum percentage of pairwise occurrences required for including a predictor in the model.

• The minimum percentage of singleton occurrences required for including a predictor in the model .

The default thresholds work well for most models, so you need not adjust these settings.

See Also:

DBMS_DATA_MINING — Algorithm Settings: Naive Bayes for a listing and explanation of the available model settings.

Note:

The term hyperparameter is also interchangeably used for model setting.