Spark ML - Naive-Bayes
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. |
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
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)