train_survival
Train and store a survival model to be loaded at a later point for prediction.
Trains a survival model using the Cox Proportional Hazard model.
The output will always be a new column with the trained model’s predictions on the training data, as well as a saved and named model file that can be used in other projects for prediction of new data.
Usage
The following shows how the step can be used in a recipe.
General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.
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 survival model to train. “CoxPH” trains a lifelines Cox Proportional Hazard model.
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).
Configure the kind of predictions to return.
Model parameters.
Was this page helpful?