FAQ

Why did I get training outputs but no global_explanations.csv?

The current implementation only generates global_explanations.csv when generate_explanations: true.

Current behavior:

• If generate_explanations is false, no global explanations file is written.

• If generate_explanations is true, the operator first tries model-derived importance when the selected model exposes it.

• knn does not expose built-in feature importance.

• For models without built-in importance, keep generate_explanations: true and make sure shap is installed.

Why did I not get test_metrics.csv?

test_metrics.csv is written only when:

• test_data is provided

• the test dataset includes the same feature columns as training

• the test dataset also includes target_column

Why does a simple explicit-model run take longer than expected?

Explicit models use Optuna-based tuning by default. If you want a faster validation run, set:

model_kwargs:
  tuning_n_trials: 0

Why does auto not use my explicit model_kwargs?

In the current implementation, auto compares the candidate models using its own candidate-selection path and then retrains the selected model. User-supplied explicit-model model_kwargs are not used during the candidate comparison stage.

When should I override column_types?

Override column_types when automatic inference is likely to be misleading, for example:

• identifier-like numeric columns such as customer_id or zip_code

• date columns stored as strings

• numeric values stored as text with inconsistent formatting

Why did training fail after I disabled preprocessing?

The current preprocessing pipeline always converts the final feature matrix to numeric form before fitting. If you disable preprocessing or categorical encoding while raw string categorical columns remain, the matrix may no longer be numeric and model training can fail.