feat(model): Train LightGBM model and log ablation to MLflow

[olveirap]

Fixes #58, Closes #17

This PR:

• Adds coverage_80pct to LightGBM's cross-validation metrics.
• Computes mean metrics for cross-validation runs in run_train.py.
• Incorporates an explicit ablation study in run_train.py where LightGBM is trained without congestion features (travel_time_var, delay_index, disruption_flag).
• Saves the trained model to models/lgbm_forecaster.pkl.
• Sets an experimental tag for the TFT model run.
• Updates RESULTS.md with the ablation test numbers.
• Updates .gitignore to un-ignore pulsecast/models/.

[gemini-code-assist]

Summary of Changes

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This pull request significantly refines the model training pipeline by introducing an ablation study for the LightGBM model, allowing for a clearer understanding of feature importance. It also enhances the robustness of MLflow tracking by logging mean cross-validation metrics and explicitly tagging experimental runs. These changes contribute to a more transparent and analytically sound machine learning development process.

Highlights

• LightGBM Model Enhancements: Added 'coverage_80pct' to LightGBM's cross-validation metrics and implemented logging of mean metrics across CV folds.
• Ablation Study Integration: Introduced an explicit ablation study for LightGBM, training a version without congestion-related features (travel_time_var, delay_index, disruption_flag) to assess their impact.
• MLflow Tracking Improvements: Enhanced MLflow logging by setting an 'experimental' tag for TFT model runs and saving the trained LightGBM model as an artifact.
• Data Feature Refinement: Updated the TFT model to use more specific feature names like 'origin_delay_index_lag1' and 'dest_delay_index_lag1', and ensured 'route_id' is cast to UTF-8 for proper handling.
• Documentation and Configuration Updates: Updated 'RESULTS.md' with new LightGBM performance numbers and marked TFT as experimental, and modified '.gitignore' to correctly manage the 'models/' directory.

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[gemini-code-assist]

Code Review

This pull request effectively introduces an ablation study for the LightGBM model, enhancing the model evaluation process. The changes to the training script improve modularity and MLflow logging by calculating and logging mean cross-validation metrics. The addition of the 80% coverage interval metric is also a valuable improvement. I have one suggestion regarding the maintainability of feature selection for the ablation study. Overall, this is a solid contribution to the project's MLOps capabilities.