Enhanced the AutoML Pipeline
heyyy guyss…hope yall are doin great…so
ive spent this week improving IntelliML’s AutoML pipeline by making model evaluation more detailed and reliable. Added support for precision, recall, and F1-score alongside accuracy, along with confusion matrices and classification reports for better performance analysis. Implemented 5 fold cross validation to provide more robust evaluation and added feature importance for tree-based models to improve model interpretability. I also integrated GridSearchCV for automatic hyperparameter tuning of Random Forest, measured model training time, and expanded the results to include detailed performance metrics for every trained algorithm. To make the pipeline more robust, I added exception handling so that if one model fails, the remaining models continue training without interrupting the workflow. With these improvements, IntelliML now provides a much more complete and reliable AutoML experience, and I’m now moving on to the visualization phase, started with correlation heatmaps.