*Hyper-parameter is a type of parameter for a machine learning model whose value is set before the model training process starts.*

Most of the algorithm including Logistic Regression deals with useful hyper parameters.

In this post we are going to discuss about the sklearn implementation of hyper-parameters for Logistic Regression. Below is the list of top hyper-parameters for Logistic regression.

**Penalty**: This hyper-parameter is used to specify the type of normalization used. Few of the values for this hyper-parameter can be l1, l2 or none. The default value is l2.**Inverse of regularization**: This hyper-parameter is denoted as C. Smaller values of this hyper-parameter indicates a stronger regularization. Default value is 1.0**Random state**: random_state is the seed used by the random number generator. Default value is None.**Solver**: This indicates which algorithm to use in the optimization problem. Default value is lbfgs. other possible values are newton-cg, liblinear, sag, saga.**Max iter**: max_iter represents maximum number of iterations taken for the solvers to converge a training process.

I hope you enjoyed reading this post. Happy Learning !!

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