# Spark ML -- OneVsRest

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.

```
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 |

formula | Used when |

classifier | Object of class |

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 |

label_col | Label column name. The column should be a numeric column. Usually this column is output by |

prediction_col | Prediction column name. |

uid | A character string used to uniquely identify the ML estimator. |

... | Optional arguments; currently unused. |

## 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`