# Spark ML -- Isotonic Regression

Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.

```
ml_isotonic_regression(x, formula = NULL, feature_index = 0,
isotonic = TRUE, weight_col = NULL, features_col = "features",
label_col = "label", prediction_col = "prediction",
uid = random_string("isotonic_regression_"), ...)
```

## Arguments

x | A |

formula | Used when |

feature_index | Index of the feature if |

isotonic | Whether the output sequence should be isotonic/increasing (true) or antitonic/decreasing (false). Default: true |

weight_col | The name of the column to use as weights for the model fit. |

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; see Details. |

## 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_estimator`

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`

.

## 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.

## 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_linear_regression`

,
`ml_linear_svc`

,
`ml_logistic_regression`

,
`ml_multilayer_perceptron_classifier`

,
`ml_naive_bayes`

,
`ml_one_vs_rest`

,
`ml_random_forest_classifier`

## Examples

```
if (FALSE) {
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
iso_res <- iris_tbl %>%
ml_isotonic_regression(Petal_Length ~ Petal_Width)
pred <- ml_predict(iso_res, iris_test)
pred
}
```