Logistic Regression Hyperparameters || Logistic Regression Part 8

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 ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected]