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Go to sklearn.Logisticregression and see there is lots of parameters within that method that parameters value can be changed. Putting the different different value of that parameters is basically is the hyperparameter tuning
In machine learning, hyperparameters are parameters that are set by the practitioner, rather than being learned from data. They are used to control the learning process and the behavior of the model. For example, in a neural network, the learning rate, the number of hidden units, and the type of activation function are all hyperparameters. Changing the values of the hyperparameters can affect the performance of the model. For example, increasing the learning rate may cause the model to converge faster, but it may also make the model more prone to overfitting. It is common to tune the hyperparameters of a model to optimize its performance on a particular task.
Running random forest algorithm with one variable