package sklearn

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package sklearn

sklearn
- Sklearn

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Library
Module
Module type
Parameter
Class
Class type

module LinearSVC : sig ... end

module LinearSVR : sig ... end

module NuSVC : sig ... end

module NuSVR : sig ... end

module OneClassSVM : sig ... end

module SVC : sig ... end

module SVR : sig ... end

val l1_min_c : 
  ?loss:[ `Squared_hinge | `Log ] ->
  ?fit_intercept:bool ->
  ?intercept_scaling:float ->
  x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
  y:Ndarray.t ->
  unit ->
  float

Return the lowest bound for C 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 or sparse matrix of shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features.

y : array, 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 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