model-training

name	model-training
description	Model training for data scientists — delegates to ds-model-trainer agent. Use when training classification or regression models, tuning hyperparameters with Optuna, running cross-validation, or comparing multiple algorithms.

Model Training Command

Train, tune, and compare ML models with cross-validation and experiment logging

Usage

/ds-model-training <description-or-file>

Examples

/ds-model-training "Binary classification for customer churn with XGBoost"
/ds-model-training "Regression model for house price prediction — tune with Optuna"
/ds-model-training "Compare RandomForest vs LightGBM vs LogisticRegression on this dataset"
/ds-model-training notebooks/training_spec.md

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What This Command Does

1. Invokes the ds-model-trainer agent
2. Reads dataset description or spec to identify problem type and target
3. Loads KB patterns from scikit-learn and xgboost domains
4. Generates:
   • Baseline model (DummyClassifier / mean predictor)
   • Primary model with cross-validation (stratified KFold or TimeSeriesSplit)
   • Optuna hyperparameter search with MLflow nested run logging
   • Model comparison table (AUC, F1, RMSE per fold)

Agent Delegation

Agent	Role
ds-model-trainer	Primary — estimators, cross-val, Optuna tuning, model comparison
ds-experiment-tracker	Escalation — when runs must be logged to MLflow
ds-model-evaluator	Escalation — when full evaluation suite is needed after training
ds-time-series-analyst	Escalation — when target is a time series

KB Domains Used

• scikit-learn — estimators, cross-validation, model selection
• xgboost — XGBoost and LightGBM training patterns
• mlflow — autologging, run logging, experiment tracking

Output

The agent generates training code with cross-validation, a model comparison table, and the best-performing pipeline object ready for evaluation.