# Spark ML -- Survival Regression

Fit a parametric survival regression model named accelerated failure time (AFT) model (see Accelerated failure time model (Wikipedia)) based on the Weibull distribution of the survival time.

```
ml_aft_survival_regression(x, formula = NULL, censor_col = "censor",
quantile_probabilities = list(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95,
0.99), fit_intercept = TRUE, max_iter = 100L, tol = 1e-06,
aggregation_depth = 2L, quantiles_col = NULL, features_col = "features",
label_col = "label", prediction_col = "prediction",
uid = random_string("aft_survival_regression_"), ...)
ml_survival_regression(x, formula = NULL, censor_col = "censor",
quantile_probabilities = list(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95,
0.99), fit_intercept = TRUE, max_iter = 100L, tol = 1e-06,
aggregation_depth = 2L, quantiles_col = NULL, features_col = "features",
label_col = "label", prediction_col = "prediction",
uid = random_string("aft_survival_regression_"), response = NULL,
features = NULL, ...)
```

## Arguments

x | A |

formula | Used when |

censor_col | Censor column name. The value of this column could be 0 or 1. If the value is 1, it means the event has occurred i.e. uncensored; otherwise censored. |

quantile_probabilities | Quantile probabilities array. Values of the quantile probabilities array should be in the range (0, 1) and the array should be non-empty. |

fit_intercept | Boolean; should the model be fit with an intercept term? |

max_iter | The maximum number of iterations to use. |

tol | Param for the convergence tolerance for iterative algorithms. |

aggregation_depth | (Spark 2.1.0+) Suggested depth for treeAggregate (>= 2). |

quantiles_col | Quantiles column name. This column will output quantiles of corresponding quantileProbabilities if it is set. |

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. |

response | (Deprecated) The name of the response column (as a length-one character vector.) |

features | (Deprecated) The name of features (terms) to use for the model fit. |

## 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`

.

## Details

`ml_survival_regression()`

is an alias for `ml_aft_survival_regression()`

for backwards compatibility.

## 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_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_one_vs_rest`

,
`ml_random_forest_classifier`