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.