Logistic Regression Hyperparameters

Optimize the hyperparameters of the scikit-learn Logistic Regression classifier. A custom training dataset can be used, and the features are standardized before training.

The searched hyperparameters are:

1. Regularization strength (C).
2. Maximum iterations.

The solver is chosen on the form rather than searched, and the penalty stays at the default (L2). At least one hyperparameter must be selected for optimization.

The objective is to find the best set of hyperparameters that increases the classification accuracy.