R: Spark ML - OneVsRest

ml_one_vs_rest {sparklyr} | R Documentation

## Spark ML – OneVsRest

### Description

Reduction of Multiclass Classification to Binary Classification. Performs reduction using one against all strategy. For a multiclass classification with k classes, train k models (one per class). Each example is scored against all k models and the model with highest score is picked to label the example.

### Usage

    ml_one_vs_rest(x, formula = NULL, classifier, features_col = "features",
      label_col = "label", prediction_col = "prediction",
      uid = random_string("one_vs_rest_"), ...)

### 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.
`classifier` | Object of class `ml_estimator`. Base binary classifier that we reduce multiclass classification into.
`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.
`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_predictor` 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_naive_bayes`, `ml_random_forest_classifier`

[Package sparklyr version 0.8.2 Index]