The training script creates separate model files for each task
(match_winner, map_winner, score_team1, score_team2, round_diff, total_maps)
so DVC needs to track each file individually.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Changed from tracking entire models/ directory to specific model file
to resolve conflict with models/metrics.json metric tracking.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Created comprehensive multi-objective modeling system:
**6 Prediction Tasks:**
1. Match Winner (Binary Classification) - Who wins the match?
2. Map Winner (Binary Classification) - Who wins this specific map?
3. Team 1 Score (Regression) - Predict exact round score for team 1
4. Team 2 Score (Regression) - Predict exact round score for team 2
5. Round Difference (Regression) - Predict score margin
6. Total Maps (Regression) - Predict number of maps in match
**Implementation:**
- Updated preprocessing to generate all target variables
- Created train_multitask.py with separate models per task
- Classification tasks use Random Forest Classifier
- Regression tasks use Random Forest Regressor
- All models logged to MLflow experiment 'csgo-match-prediction-multitask'
- Metrics tracked per task (accuracy/precision for classification, MAE/RMSE for regression)
- Updated DVC pipeline to use new training script
**No Data Leakage:**
- All features are pre-match only (rankings, map, starting side)
- Target variables properly separated and saved with 'target_' prefix
This enables comprehensive match analysis and multiple betting/analytics use cases.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
