Creates a naive Bayes model. It supports Multinomial NB which can handle finitely supported discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a binary (0/1) data, it can also be used as Bernoulli NB. The input feature values must be nonnegative.
This operation is ported from Spark ML.
For a comprehensive introduction, see Spark documentation.
For scala docs details, see org.apache.spark.ml.classification.NaiveBayes documentation.
Since: Seahorse 1.1.0
This operation does not take any input.
| Port | Type Qualifier | Description |
|---|---|---|
0 | Estimator | An Estimator that can be used in a Fit operation. |
| Name | Type | Description |
|---|---|---|
smoothing |
Numeric |
The smoothing parameter. |
modelType |
SingleChoice |
The model type. Possible values: ["multinomial", "bernoulli"] |
label column |
SingleColumnSelector |
The label column for model fitting. |
features column |
SingleColumnSelector |
The features column for model fitting. |
probability column |
String |
The column for predicted class conditional probabilities. |
raw prediction column |
String |
The raw prediction (confidence) column. |
prediction column |
String |
The prediction column created during model scoring. |