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Logistic regression class in sklearn comes with L1 and L2 regularization. How can I turn off regularization to get the "raw" logistic fit such as in glmfit in Matlab? I think I can set C = large number but I don't think it is wise.
see for more details the documentation http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression
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Linear models are usually a good starting point for training a model. However, a lot of datasets do not exhibit linear relationships between the independent and the dependent variables. As a result, it is frequently necessary to create a polynomial model.
By default, logistic regression in scikit-learn runs w L2 regularization on and defaulting to magic number C=1.0.
In order to avoid overfitting, it is necessary to use additional techniques (e.g. cross-validation, regularization, early stopping, pruning, or Bayesian priors).
Comparison of the sparsity (percentage of zero coefficients) of solutions when L1, L2 and Elastic-Net penalty are used for different values of C. We can see that large values of C give more freedom to the model. Conversely, smaller values of C constrain the model more.
Yes, choose as large a number as possible. In regularization, the cost function includes a regularization expression, and keep in mind that the C parameter in sklearn regularization is the inverse of the regularization strength.
C in this case is 1/lambda, subject to the condition that C > 0.
Therefore, when C approaches infinity, then lambda approaches 0. When this happens, then the cost function becomes your standard error function, since the regularization expression becomes, for all intents and purposes, 0.
Update: In sklearn versions 0.21 and higher, you can disable regularization by passing in penalty='none'. Check out the documentation here.
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Go ahead and set C as large as you please. Also, make sure to use l2 since l1 with that implementation can be painfully slow.
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