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, ...)
ml_pipeline, or 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.
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 array. Values of the quantile probabilities array should be in the range (0, 1) and the array should be non-empty.
Boolean; should the model be fit with an intercept term?
The maximum number of iterations to use.
Param for the convergence tolerance for iterative algorithms.
(Spark 2.1.0+) Suggested depth for treeAggregate (>= 2).
Quantiles column name. This column will output quantiles of corresponding quantileProbabilities if it is set.
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 column name. The column should be a numeric column. Usually this column is output by
Prediction column name.
A character string used to uniquely identify the ML estimator.
Optional arguments; see Details.
(Deprecated) The name of the response column (as a length-one character vector.)
(Deprecated) The name of features (terms) to use for the model fit.
x is a
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_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
type = "pipeline" to faciliate model refresh workflows.
ml_survival_regression() is an alias for
ml_aft_survival_regression() for backwards compatibility.
The object returned depends on the class of
spark_connection, the function returns an instance of a
ml_predictorobject. The object contains a pointer to a Spark
Predictorobject and can be used to compose
ml_pipeline, the function returns a
ml_pipelinewith the predictor appended to the pipeline.
tbl_spark, a predictor is constructed then immediately fit with the input
tbl_spark, returning a prediction model.
formula: specified When
formulais specified, the input
tbl_sparkis first transformed using a
RFormulatransformer before being fit by the predictor. The object returned in this case is a
ml_modelwhich is a wrapper of a
See http://spark.apache.org/docs/latest/ml-classification-regression.html for more information on the set of supervised learning algorithms.
Other ml algorithms: