API reference¶
Survival Support Vector Machine¶
FastKernelSurvivalSVM([alpha, rank_ratio, ...]) |
Efficient Training of kernel Survival Support Vector Machine. |
FastSurvivalSVM([alpha, rank_ratio, ...]) |
Efficient Training of linear Survival Support Vector Machine |
MinlipSurvivalAnalysis([solver, alpha, ...]) |
Survival model related to survival SVM, using a minimal Lipschitz smoothness strategy instead of a maximal margin strategy. |
HingeLossSurvivalSVM([solver, alpha, ...]) |
Naive implementation of kernel survival support vector machine. |
NaiveSurvivalSVM([penalty, loss, dual, tol, ...]) |
Naive version of linear Survival Support Vector Machine. |
Kernels¶
clinical_kernel(x[, y]) |
Computes clinical kernel |
ClinicalKernelTransform([fit_once, ...]) |
Transform data using a clinical Kernel |
Metrics¶
concordance_index_censored(event_indicator, ...) |
Concordance index for right-censored data |
Pre-Processing¶
categorical_to_numeric(table) |
Encode categorical columns to numeric by converting each category to an integer value. |
encode_categorical(table, **kwargs) |
Encode categorical columns with M categories into M-1 columns according to the one-hot scheme. |
standardize(table[, with_std]) |
Perform Z-Normalization on each numeric column of the given table. |