predict_survival
Use a pretrained model to predict new data.
Usage
The following examples show how the step can be used in a recipe.
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").
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).
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 percentile
Values must be in the following range:
Points in time to predict.
Configures at which points to predict when kind is set to survival_function.
Either an explicit array of durations, or an object specifying a duration step size and
maximum duration.
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.
Target variables. Two names, exactly, corresponding to the target columns that contain in the following order:
- whether the event was observed (boolean) and
- the time (duration) to event or censoring (number).
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