sksurv.linear_model.
IPCRidge
Accelerated failure time model with inverse probability of censoring weights.
This model assumes a regression model of the form
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).
See 1 for further description.
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_
Weight vector.
ndarray, shape = (n_features,)
References
W. Stute, “Consistent estimation under random censorship when covariables are present”, Journal of Multivariate Analysis, vol. 45, no. 1, pp. 89-103, 1993. doi:10.1006/jmva.1993.1028.
__init__
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__([alpha, fit_intercept, normalize, …])
Initialize self.
fit(X, y)
fit
Build an accelerated failure time model.
predict(X)
predict
Predict using the linear accelerated failure time model.
score(X, y[, sample_weight])
score
Returns the concordance index of the prediction.
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
X ({array-like, sparse matrix}, shape = (n_samples, n_features)) – Samples.
C – Returns predicted values on original scale (NOT log scale).
array, shape = (n_samples,)
X (array-like, shape = (n_samples, n_features)) – Test samples.
cindex – Estimated concordance index.
float