Satanas2

[sandragc8]

Enhanced Argument Parsing:

The script now includes a more comprehensive set of command-line arguments, allowing for greater flexibility in configuring model training and evaluation. New arguments have been added to specify the proportion of data for testing (--test_size), enable cross-validation (--use_cross_validation), and define the number of folds for cross-validation (--cv_folds). Additionally, users can select the type of model to use (--model_type), specify the kernel type for SVM (--kernel), and adjust regularization parameters (--C and --gamma). Options for polynomial features (--use_poly_features and --degree) and hyperparameter tuning (--tune_hyperparameters) have also been introduced. Furthermore, arguments for specifying file paths to save the trained model (--output_model) and evaluation results (--output_results) have been included.

Updated train_and_evaluate Function:

The train_and_evaluate function has been modified to incorporate the new command-line arguments, allowing for more flexible model configuration. The function now supports setting model parameters dynamically based on user input. It also includes functionality for saving the trained model to a file using joblib and storing evaluation results in a specified text file. This enhancement makes it easier to manage and review model performance and outputs.

Output Management Improvements:

To facilitate better tracking and persistence of model training results, the script now saves the trained model and the evaluation report to user-specified files. This improvement ensures that users can efficiently manage and archive their machine learning experiments, providing a streamlined way to handle model artifacts and results.