# sksurv.linear_model.IPCRidge¶

class sksurv.linear_model.IPCRidge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto')

Accelerated failure time model with inverse probability of censoring weights.

This model assumes a regression model of the form

$\log y = \beta_0 + \mathbf{X} \beta + \epsilon$

L2-shrinkage is applied to the coefficients $$\beta$$ and each sample is weighted by the inverse probability of censoring to account for right censoring (under the assumption that censoring is independent of the features, i.e., random censoring).

Parameters: alpha : float, optional, default: 1.0 Small positive values of alpha improve the conditioning of the problem and reduce the variance of the estimates. coef_ : ndarray, shape = (n_features,) Weight vector.
__init__(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto')

Methods

 __init__([alpha, fit_intercept, normalize, …]) fit(X, y) Build an accelerated failure time model. predict(X) Predict using the linear accelerated failure time model. score(X, y[, sample_weight])
fit(X, y)

Build an accelerated failure time model.

Parameters: X : array-like, shape = (n_samples, n_features) Data matrix. y : structured array, shape = (n_samples,) A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field. self
predict(X)

Predict using the linear accelerated failure time model.

Parameters: X : {array-like, sparse matrix}, shape = (n_samples, n_features) Samples. C : array, shape = (n_samples,) Returns predicted values on original scale (NOT log scale).