Linear Scaling Binary Classification Model

class caliber.binary_classification.minimizing.linear_scaling.calibration.asce_linear_scaling.ASCELinearScalingBinaryClassificationModel(minimize_options=None, lam=0.01, has_intercept=True, has_bivariate_slope=False, num_features=0)[source]
fit(probs, targets)
predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.calibration.ece_linear_scaling.ECELinearScalingBinaryClassificationModel(minimize_options=None, lam=0.01, has_intercept=True, has_bivariate_slope=False, num_features=0)[source]
fit(probs, targets)
predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.calibration.brier_linear_scaling.BrierLinearScalingBinaryClassificationModel(minimize_options=None, has_intercept=True, has_bivariate_slope=False, num_features=0)[source]
fit(probs, targets, features=None)
Return type:

dict

predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.calibration.cross_entropy_linear_scaling.CrossEntropyLinearScalingBinaryClassificationModel(minimize_options=None, has_intercept=True, has_bivariate_slope=False, num_features=0)[source]
fit(probs, targets, features=None)
Return type:

dict

predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.calibration.focal_linear_scaling.FocalLinearScalingBinaryClassificationModel(minimize_options=None, has_intercept=True, has_bivariate_slope=False, num_features=0, gamma=2.0)[source]
fit(probs, targets, features=None)
Return type:

dict

predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.calibration.knee.KneePointLinearScalingBinaryClassificationModel(minimize_options=None, lam=0.01, has_intercept=True, has_bivariate_slope=False, num_features=0, n_thresholds=100)[source]
fit(probs, targets)
predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.calibration.precrec.PrecisionRecallLinearScalingBinaryClassificationModel(threshold=None, lam=0.01, minimize_options=None, has_intercept=True, num_features=0, n_thresholds=100)[source]
fit(probs, targets)
predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.calibration.beta.BetaBinaryClassificationModel(minimize_options=None, num_features=0)[source]
fit(probs, targets, features=None)
Return type:

dict

predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.calibration.beta.DiagBetaBinaryClassificationModel(minimize_options=None, num_features=0)[source]
fit(probs, targets, features=None)
Return type:

dict

predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.performance.bal_acc_linear_scaling.BalancedAccuracyLinearScalingBinaryClassificationModel(threshold, lam=0.01, minimize_options=None, has_intercept=True, num_features=0)[source]
fit(probs, targets)
predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.performance.f1_linear_scaling.PositiveF1LinearScalingBinaryClassificationModel(threshold, lam=0.01, minimize_options=None, has_intercept=True, num_features=0)[source]
fit(probs, targets)
predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.performance.f1_linear_scaling.NegativeF1LinearScalingBinaryClassificationModel(threshold, lam=0.01, minimize_options=None, has_intercept=True, num_features=0)[source]
fit(probs, targets)
predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.performance.positive_negative_rates_linear_scaling.PositiveNegativeRatesLinearScalingBinaryClassificationModel(threshold, lam=0.01, minimize_options=None, has_intercept=True, num_features=0)[source]
fit(probs, targets)
predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.performance.precision_fixed_recall_linear_scaling.PrecisionFixedRecallLinearScalingBinaryClassificationModel(threshold, lam=0.01, minimize_options=None, has_intercept=True, num_features=0, min_recall=0.8)[source]
fit(probs, targets)
predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.performance.recall_fixed_precision_linear_scaling.RecallFixedPrecisionLinearScalingBinaryClassificationModel(threshold, lam=0.01, minimize_options=None, has_intercept=True, num_features=0, min_precision=0.8)[source]
fit(probs, targets)
predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray

class caliber.binary_classification.minimizing.linear_scaling.performance.righteousness_linear_scaling.RighteousnessLinearScalingBinaryClassificationModel(threshold, lam=0.01, minimize_options=None, has_intercept=True, num_features=0)[source]
fit(probs, targets)
predict(probs, features=None)
Return type:

ndarray

predict_proba(probs, features=None)
Return type:

ndarray