Patient Specific Survival Prediction - Option Details

This page provides details about options available to you for training a PSSP predictor

Feature Standardization

Checking this option will standardize each feature in your data set. This will generally speed up training, and sometimes increase accuracy.


Checking this option will run cross-validation on your data set. This will allow you to examine your training data, and get an estimate of your predictor's accuracy.

Tuning Parameters

Checking this option will try to improve the accuracy of your predictor by evaluating various parameter settings. This will increase training time.

Feature Imputation

If your data set contains missing values, they will be replaced with the column mean.


This option specifies the penalty term in the optimization function. Available regularizations are: L1, and L2. L1 regularization will result in a sparse feature set (i.e. a predictor that uses less of the features), but the time to train a predictor is longer.

Smoothed Log-Likelihood

This option uses a smoothed PDF function to calculate the log-likelihood, and also uses this to tune parameters.