predict_survival
Use a pretrained model to predict new data.
Usage
The following examples show how the step can be used in a recipe.Examples
Examples
Without configuration, the median survival time is returned.
Inputs & Outputs
The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced
by name e.g. "churn-clf").
Inputs
Inputs
Outputs
Outputs
Column containing the model predictions.
Configuration
The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e.step(..., {"param": "value", ...}) -> (output).
Parameters
Parameters
Kind of prediction.
median returns the median survival time. percentile returns the survival time at
the given percentile. expectation returns the expected survival time.
survival_function returns the whole survival function (one series per sample).Values must be one of the following:medianpercentileexpectationsurvival_function
Percentile when
kind is set to percentileValues must be in the following range:Whether to predict remaining time.
Conditions the predictions on known durations. In other words, the prediction is made for
each sample taking into account that the sample has survived up to the duration in this column, and
the prediction is made for the remaining time. This applies only to censored samples, where
the event has not been observed. If the event has already been observed, on the other hand, predicted
remaining time will be 0 /
null.To use this feature, the target parameter must also be provided to identify the event and duration
columns in the dataset.