LogisticRegression

LogisticRegression(
    alpha=0.0,
    l1_ratio=0.0,
    fit_intercept=True,
    max_iterations=1000,
    tolerance=1e-08,
    l1_smoothing=1e-06,
)

Binary logistic regression estimated by maximum likelihood.

The model minimizes the negative Bernoulli log-likelihood. For regularized fits, the optimizer switches to Frank-Wolfe over an exact L1 or L2 ball. For unregularized fits, the class exposes classical inverse-information covariance estimates and robust QMLE sandwich covariance estimates.

Attributes

Name	Type	Description
coef_	ndarray of shape (n_features,)	Estimated slope coefficients.
intercept_	float	Estimated intercept. Equals 0.0 when fit_intercept=False.
params_	ndarray of shape (n_params,)	Full parameter vector, including the intercept when present.
std_errors_	ndarray or None	Classical standard errors for params_.
robust_std_errors_	ndarray or None	Robust QMLE standard errors for params_.

Methods

Name	Description
confidence_intervals	Return coefficient confidence intervals.
fit	Fit the logistic regression model.
predict	Predict binary class labels using a 0.5 threshold.
predict_proba	Return class probabilities for the binary response.
score	Return mean classification accuracy on the provided sample.
summary	Return [coef, se, ci_lb, ci_ub] for each fitted parameter.

confidence_intervals

LogisticRegression.confidence_intervals(alpha=0.05, covariance_type='nonrobust')

Return coefficient confidence intervals.

fit

LogisticRegression.fit(X, y)

Fit the logistic regression model.

Parameters

Name	Type	Description	Default
X	ndarray of shape (n_samples, n_features)	Feature matrix.	required
y	ndarray of shape (n_samples,)	Binary response encoded as 0 or 1.	required

Returns

Name	Type	Description
	LogisticRegression	The fitted estimator.

predict

LogisticRegression.predict(X)

Predict binary class labels using a 0.5 threshold.

predict_proba

LogisticRegression.predict_proba(X)

Return class probabilities for the binary response.

Parameters

Name	Type	Description	Default
X	ndarray of shape (n_samples, n_features)	Feature matrix.	required

Returns

Name	Type	Description
	ndarray of shape (n_samples, 2)	Column-stacked probabilities for classes 0 and 1.

score

LogisticRegression.score(X, y)

Return mean classification accuracy on the provided sample.

summary

LogisticRegression.summary(alpha=0.05, covariance_type='nonrobust')

Return [coef, se, ci_lb, ci_ub] for each fitted parameter.