Logistic Regression Hyperparameters || Logistic Regression Part 8
Channel: CampusX
Date: 2021-06-30

Logistic Regression hyperparameters are parameters that are set before the training process and influence the model's performance. Key hyperparameters include 'C' for regularization strength, 'solver' for optimization algorithm, and 'max_iter' for the maximum number of iterations. Fine-tuning these hyperparameters is crucial for achieving optimal Logistic Regression model performance on specific datasets.

Code used : https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day60-logistic-regression-contd

Links: 
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

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