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scikit-learn 1.3.2
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# `sklearn.svm`.l1_min_c

sklearn.svm.l1_min_c(X, y, *, loss='squared_hinge', fit_intercept=True, intercept_scaling=1.0)

  Return the lowest bound for C.

  The lower bound for C is computed such that for C in (l1_min_C, infinity) the model is guaranteed not to be empty. This applies to l1 penalized classifiers, such as LinearSVC with penalty=’l1’ and linear_model.LogisticRegression with penalty=’l1’.

  This value is valid if class_weight parameter in fit() is not set.

  Parameters:
    X{array-like, sparse matrix} of shape (n_samples, n_features)

      Training vector, where `n_samples` is the number of samples and `n_features` is the number of features.

    y array-like of shape (n_samples,)

      Target vector relative to X.

    loss{‘squared_hinge’, ‘log’}, default=’squared_hinge’

      Specifies the loss function. With ‘squared_hinge’ it is the squared hinge loss (a.k.a. L2 loss). With ‘log’ it is the loss of logistic regression models.

    fit_intercept bool, default=True

      Specifies if the intercept should be fitted by the model. It must match the fit() method parameter.

    intercept_scaling float, default=1.0

      When fit_intercept is True, instance vector x becomes [x, intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. It must match the fit() method parameter.

  Returns:
    l1_min_c float

      Minimum value for C.

## Examples using `sklearn.svm.l1_min_c`

Regularization path of L1- Logistic Regression

Regularization path of L1- Logistic Regression