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- The `compute_metrics` function computes accuracy and F1-score using Scikit-learn's `accuracy_score` and `f1_score`. 2. **Collect Data**: - We use `make_classification` to generate synthetic data for demonstration purposes. In a real-world scenario, you would use your own dataset. 3. **Implement Automated Testing**: - The `train_and_evaluate_model` function trains a logistic regression model and evaluates it using the defined metrics. 4. **Track and Improve Metrics**: - The `track_metrics` function runs the training and evaluation process multiple times, logs the results, and computes the average metrics over the iterations. ### Logging and Analysis - **Logging**: The `logging` module is used to log the metrics at each iteration and the average metrics over all iterations. - **Analysis**: By analyzing the logged metrics, you can identify trends and areas for improvement. You can then refine your models or data preprocessing steps accordingly. ### Next Steps - **Refine Models**: Based on the logged metrics, you can experiment with different models, hyperparameters, or feature engineering techniques to improve the metrics.