Week 19 – Fine-Tuning and Optimizing Kaggle Models

Dates: October 5 – October 11
Internship: AI/ML Intern at SynerSense Pvt. Ltd.
Mentor: Praveen Kulkarni Sir

Focus

Building on last week’s Kaggle experiments, this week centered around model fine-tuning and optimization to improve performance metrics, particularly reducing RMSE on regression tasks and improving overall generalization.
The goal was to transition from functional models to production-ready pipelines capable of robust and consistent predictions.

Goals for the Week

  • Fine-tune the models developed for Kaggle challenges
  • Reduce Root Mean Squared Error (RMSE) and other validation metrics
  • Optimize hyperparameters using grid and random search
  • Explore ensemble and stacking techniques for performance boosting
  • Document reproducible pipelines and performance tracking

Tasks Completed

Task Status Notes
Hyperparameter tuning on regression models ✅ Completed Used GridSearchCV and Optuna for tuning key parameters
Reduced RMSE for “House Prices” model ✅ Completed RMSE improved from 0.142 to 0.125 after fine-tuning
Fine-tuned CNN architecture on “Digit Recognizer” ✅ Completed Enhanced accuracy and reduced overfitting
Implemented model stacking (XGBoost + LightGBM) ✅ Completed Achieved consistent performance gains
Automated result logging for experiments ✅ Completed Created a reusable pipeline using MLflow and pandas

Key Learnings

  • Fine-tuning requires balance. Over-optimization can lead to marginal gains but increased model complexity.
  • RMSE improvement is not only about model choice. Careful feature selection and preprocessing can make a significant difference.
  • Model stacking effectively combines strengths of multiple algorithms for better generalization.
  • Experiment tracking is essential — every improvement needs documentation for reproducibility.

Challenges and Solutions

Challenge Solution
High RMSE despite complex models Simplified feature set and applied feature scaling
Overfitting during fine-tuning Used K-fold cross-validation and early stopping
Long training times during grid search Switched to Optuna’s Bayesian optimization
Tracking multiple experiments Automated run logging with timestamps and metrics

References

Goals for Next Week

  • Consolidate final reports and visualizations
  • Summarize key findings across all Kaggle experiments
  • Prepare the final internship report and presentation
  • Reflect on overall skill development during the internship

Screenshots (Optional)

Screenshots of RMSE plots, Optuna tuning graphs, and stacked model comparisons.

“Week 19 was about precision — refining models until they stopped just working and started performing.”