# How to optimise hyper-parameters of a Logistic Regression Model using GridSearchCV in Python

When building a machine learning model, it’s important to optimize the parameters of the model for the best performance. One way to do this is by tuning the hyper-parameters of a logistic regression model using GridSearchCV.

Logistic regression is a statistical method that is used to predict the probability of an outcome occurring. It is commonly used in binary classification problems. The hyper-parameters of a logistic regression model include the regularization term (C) and the solver (solver).

GridSearchCV is a method that allows you to search for the best combination of hyper-parameters, by training and evaluating a model using different combinations of hyper-parameters. It will pick the best combination of hyper-parameters based on the performance of the model.

In Python, the library scikit-learn provides an easy way to perform GridSearchCV using the function `GridSearchCV()`

.

The first step is to import the library and load the dataset into a pandas dataframe. Then, split the data into training and testing sets, and create an instance of the logistic regression model you want to evaluate.

After that, you can use the `GridSearchCV()`

function, which takes the logistic regression model, the dataset, a dictionary of hyper-parameters (C and solver) and their possible values, and the scoring metric as inputs. The function returns the best combination of hyper-parameters based on the performance of the model.

You can use the `cv`

parameter that takes the number of splits you would like to make, or an iterable that you can use to define the splits.

Additionally, you can use the `refit`

parameter, it will refit the estimator with best hyper-parameters using all available data.

In summary, GridSearchCV is a powerful tool for tuning the hyper-parameters of a logistic regression model.

In this Learn through Codes example, you will learn: How to optimise hyper-parameters of a Logistic Regression Model using GridSearchCV in Python.

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