```
library(sparklyr)
<- spark_connect(master = "local")
sc <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
iris_tbl
<- iris_tbl %>%
partitions sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
<- partitions$training
iris_training <- partitions$test
iris_test
<- iris_training %>%
nb_model ml_naive_bayes(Species ~ .)
<- ml_predict(nb_model, iris_test)
pred
ml_multiclass_classification_evaluator(pred)
#> [1] 0.9393939
```

# Spark ML – Naive-Bayes

*R/ml_classification_naive_bayes.R*

## ml_naive_bayes

## Description

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

Arguments | Description |
---|---|

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`

.

## Examples

## See Also

See https://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()`