# ml_naive_bayes

##### Spark ML -- Naive-Bayes

Naive Bayes Classifiers. It supports Multinomial NB (see here) 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 (see here). The input feature values must be nonnegative.

##### Usage

```
ml_naive_bayes(x, formula = NULL, model_type = "multinomial",
smoothing = 1, thresholds = NULL, weight_col = NULL,
features_col = "features", label_col = "label",
prediction_col = "prediction", probability_col = "probability",
raw_prediction_col = "rawPrediction",
uid = random_string("naive_bayes_"), ...)
```

##### Arguments

- x
A

`spark_connection`

,`ml_pipeline`

, or a`tbl_spark`

.- formula
Used when

`x`

is a`tbl_spark`

. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.- model_type
The model type. Supported options:

`"multinomial"`

and`"bernoulli"`

. (default =`multinomial`

)- smoothing
The (Laplace) smoothing parameter. Defaults to 1.

- thresholds
Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value

`p/t`

is predicted, where`p`

is the original probability of that class and`t`

is the class's threshold.- weight_col
(Spark 2.1.0+) Weight column name. If this is not set or empty, we treat all instance weights as 1.0.

- features_col
Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by

`ft_r_formula`

.- label_col
Label column name. The column should be a numeric column. Usually this column is output by

`ft_r_formula`

.- prediction_col
Prediction column name.

- probability_col
Column name for predicted class conditional probabilities.

- raw_prediction_col
Raw prediction (a.k.a. confidence) column name.

- uid
A character string used to uniquely identify the ML estimator.

- ...
Optional arguments; see Details.

##### Details

When `x`

is a `tbl_spark`

and `formula`

(alternatively, `response`

and `features`

) is specified, the function returns a `ml_model`

object wrapping a `ml_pipeline_model`

which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument `predicted_label_col`

(defaults to `"predicted_label"`

) can be used to specify the name of the predicted label column. In addition to the fitted `ml_pipeline_model`

, `ml_model`

objects also contain a `ml_pipeline`

object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by `ml_save`

with `type = "pipeline"`

to faciliate model refresh workflows.

##### Value

The object returned depends on the class of `x`

.

`spark_connection`

: When`x`

is a`spark_connection`

, the function returns an instance of a`ml_estimator`

object. The object contains a pointer to a Spark`Predictor`

object and can be used to compose`Pipeline`

objects.`ml_pipeline`

: When`x`

is a`ml_pipeline`

, the function returns a`ml_pipeline`

with the predictor appended to the pipeline.`tbl_spark`

: When`x`

is a`tbl_spark`

, a predictor is constructed then immediately fit with the input`tbl_spark`

, returning a prediction model.`tbl_spark`

, with`formula`

: specified When`formula`

is specified, the input`tbl_spark`

is first transformed using a`RFormula`

transformer before being fit by the predictor. The object returned in this case is a`ml_model`

which is a wrapper of a`ml_pipeline_model`

.

##### See Also

See http://spark.apache.org/docs/latest/ml-classification-regression.html for more information on the set of supervised learning algorithms.

Other ml algorithms: `ml_aft_survival_regression`

,
`ml_decision_tree_classifier`

,
`ml_gbt_classifier`

,
`ml_generalized_linear_regression`

,
`ml_isotonic_regression`

,
`ml_linear_regression`

,
`ml_linear_svc`

,
`ml_logistic_regression`

,
`ml_multilayer_perceptron_classifier`

,
`ml_one_vs_rest`

,
`ml_random_forest_classifier`

##### Examples

```
# NOT RUN {
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
partitions <- iris_tbl %>%
sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
iris_training <- partitions$training
iris_test <- partitions$test
nb_model <- iris_training %>%
ml_naive_bayes(Species ~ .)
pred <- ml_predict(nb_model, iris_test)
ml_multiclass_classification_evaluator(pred)
# }
# NOT RUN {
# }
```

*Documentation reproduced from package sparklyr, version 1.1.0, License: Apache License 2.0 | file LICENSE*