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.Examples
Examples
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").
Inputs
Inputs
Should contain the target columns (see
target parameter below) and the feature columns you wish to use in the model.Outputs
Outputs
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 survival model to train.
“CoxPH” trains a lifelines Cox Proportional Hazard model.
Configure the kind of predictions to return.
Properties
Properties
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: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.Model parameters.
Properties
Properties
Level in the confidence intervals.
Penalizer strength.
Attach an L2 penalizer to the size of the coefficients during regression.
This improves stability of the estimates and controls for high correlation between covariates.Values must be in the following range:
L1 vs L2 penalty ratio.
Specify what ratio to assign to a L1 vs L2 penalty (ridge vs lasso). Same as scikit-learn
convention.Values must be in the following range:
Columns to use in stratification.
This is useful if a categorical covariate does not obey the proportional hazard assumption.
Array items
Array items
Each item in array.
How the fitter should estimate the baseline.Values must be one of the following:
breslowsplinepiecewise