ml-training

# ML Model Training Patterns Reference patterns for training and deploying ML models via the Dataiku Python API. ## Quick Start: Create and Train a Prediction Model ```python # Create ML task ml_task = project.create_prediction_ml_task( input_dataset="MY_DATASET", target_variable="target_column", prediction_type="BINARY_CLASSIFICATION", # or REGRESSION, MULTICLASS ml_backend_type="PY_MEMORY", guess_policy="DEFAULT", wait_guess_complete=True ) # Train models (waits for completion) trained_ids = ml_task.train(session_name="My Training Session") # Get metrics for each trained model for model_id in trained_ids: details = ml_task.get_trained_model_details(model_id) metrics = details.get_performance_metrics() algo = details.get_modeling_settings()["algorithm"] print(f"Model: {algo}") print(f" AUC: {metrics.get('auc')}") print(f" Log Loss: {metrics.get('logLoss')}") ``` ## Deploy Model to Flow **Important:** Only deploy models when explicitly requested by the user. After training, show results and let the user decide. When deploying, confirm whether to create new or update existing. ### First-time deployment (creates new saved model) ```python result = ml_task.deploy_to_flow( model_id=best_model_id, model_name="my_model", train_dataset="MY_DATASET" ) # Returns: {"savedModelId": "...", "trainRecipeName": "..."} ``` ### Update existing saved model (new version) ```python saved_model = project.get_saved_model("my_model") sm_id = saved_model.get_id() ml_task.redeploy_to_flow(model_id=new_model_id, saved_model_id=sm_id) ``` ## Access Existing ML Task ```python analyses = project.list_analyses() analysis = project.get_analysis(analyses[0]['analysisId']) ml_tasks_info = analysis.list_ml_tasks() ml_task = analysis.get_ml_task(ml_tasks_info['mlTasks'][0]['mlTaskId']) model_ids = ml_task.get_trained_models_ids() ``` ## References - **Model metrics, get_performance_metrics(), common metrics table** — see [references/model-metrics.md](references/model-metrics.md) - **Algorithm names (UPPERCASE), enable/disable, hyperparameters** — see [references/algorithm-config.md](references/algorithm-config.md) - **Feature importance, rawImportance, dummy filtering** — see [references/feature-importance.md](references/feature-importance.md)