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 atbl_spark.- formula
Used when
xis atbl_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/tis predicted, wherepis the original probability of that class andtis 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: Whenxis aspark_connection, the function returns an instance of aml_estimatorobject. The object contains a pointer to a SparkPredictorobject and can be used to composePipelineobjects.ml_pipeline: Whenxis aml_pipeline, the function returns aml_pipelinewith the predictor appended to the pipeline.tbl_spark: Whenxis atbl_spark, a predictor is constructed then immediately fit with the inputtbl_spark, returning a prediction model.tbl_spark, withformula: specified Whenformulais specified, the inputtbl_sparkis first transformed using aRFormulatransformer before being fit by the predictor. The object returned in this case is aml_modelwhich is a wrapper of aml_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 {
# }