ml_naive_bayes: Spark ML - Naive-Bayes

In sparklyr: R Interface to Apache Spark

Spark 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

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.

Value

The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the predictor appended to it. If a tbl_spark, it will return a tbl_spark with the predictions added to it.

See Also

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)

## End(Not run)

sparklyr documentation built on May 29, 2024, 2:58 a.m.