FAQ / Troubleshooting

I got AttributeError when calling run_pipeline(..., is_train=False).

You must call the pipeline once with is_train=True before ever calling it with is_train=False, on that same instance. There is nothing learned to apply until the first training pass runs. See Core Concepts.

A column I expected is missing from my output.

Check eda.num_handler.drop_cols_ and eda.cat_handler.drop_cols_ — columns with more than 40% missing values are dropped automatically during the training pass.

How do I know why a column got a specific encoder or scaler?

See Inspecting Learned Rules — every decision is stored as a readable attribute on the handler.

My test set has a category value that never appeared in training. Will it crash?

No. Unseen categories are handled gracefully: binary columns map them to -1, one-hot columns get all zeros, and frequency-encoded columns get 0.

Can I change the 40% missing-value or 5% rare-category thresholds?

The rare-category threshold is configurable via rare_manager(threshold=...). The missing-value drop threshold (40%) and outlier threshold (3%) are currently fixed constants — see the Changelog for planned configurability.

Can I exclude an ID column from processing?

Yes for HandleNumerical, via the granular methods (num_imputer, outlier_manager, scaler) — see HandleNumerical. Note that full_handler() does not currently forward exclude.