skclean.models.RobustLR¶
-
class
skclean.models.
RobustLR
(PN=0.2, NP=0.2, C=inf, max_iter=4000, random_state=None)¶ Modifies the logistic loss using class dependent (estimated) noise rates for robustness. This implementation is for binary classification tasks only.
See [NDRT13] for details.
- Parameters
PN (float, default=.2) – Percentage of Positive labels flipped to Negative.
NP (float, default=.2) – Percentage of Negative labels flipped to Positive.
C (float) – Inverse of regularization strength, must be a positive float.
random_state (int, default=None) – Set this value for reproducibility
Methods
__init__
([PN, NP, C, max_iter, random_state])Initialize self.
decision_function
(X)Predict confidence scores for samples.
densify
()Convert coefficient matrix to dense array format.
fit
(X, y[, sample_weight])Fit the model according to the given training data.
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict class labels for samples in X.
predict_log_proba
(X)Predict logarithm of probability estimates.
predict_proba
(X)Probability estimates.
score
(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(**params)Set the parameters of this estimator.
sparsify
()Convert coefficient matrix to sparse format.