select-lambda.mdx

title	Select lambda_hyb
description	Choose a Hyb-FPDE mixture weight with held-out validation data

Use FPDEEngine.select_lambda to choose a fixed lambda_hyb before you explain final evaluation or test samples. The method evaluates candidates with deletion and insertion perturbation curves on held-out data.

Prerequisites

• A fitted FPDEEngine
• Held-out validation samples X_val
• A classifier with predict_proba and classes_
• A candidate grid for lambda_hyb

Keep validation data separate from the final reporting split.

Run selection

Start with a small grid.

```python
lambda_grid = (0.0, 0.25, 0.5, 0.75, 1.0)
```

Score each candidate on held-out samples.

```python
selection = engine.select_lambda(
    X_val,
    lambda_hyb_grid=lambda_grid,
    fractions=(0.0, 0.1, 0.3, 0.5, 0.7, 1.0),
    normalize="l1",
)
```

Explain final samples with the selected `lambda_hyb`.

```python
attributions, details = engine.explain_batch(
    X_test,
    lambda_hyb=selection.best_lambda,
    normalize="l1",
)
```

Inspect the result

print(selection.best_lambda)
print(selection.best_config)
print(selection.rows)

selection.rows contains the candidate scores. Save it with your experiment artifacts.

How selection is scored

For each candidate, FPDE computes deletion and insertion curves. Deletion replaces top-ranked features with the baseline. Insertion starts from the baseline and restores top-ranked features.

The combined score is:

combined_score = 0.5 * (deletion_drop_auc + insertion_auc)

The selected lambda is the candidate with the best validation score.

Use the same `normalize`, `anchor_strategy`, and `eps` values during selection and final explanation.