> ## Documentation Index
> Fetch the complete documentation index at: https://fpde-80-mintlify-48090872.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Validate explanations

> Use perturbation curves and sanity checks to inspect FPDE attribution quality

Validation helps you check whether an attribution vector behaves as expected for a fitted classifier.
FPDE provides deletion and insertion perturbation curves for this purpose.

## Compute perturbation curves

```python theme={null}
from fpde import perturbation_curves

attributions, details = engine.explain_one(X_test[0], lambda_hyb=0.5)

curves = perturbation_curves(
    model,
    X_test[0],
    attributions,
    details["target_label"],
    engine.baseline,
    fractions=(0.0, 0.1, 0.3, 0.5, 1.0),
)

print(curves["deletion_prob"])
print(curves["insertion_prob"])
```

Features are ranked by signed positive attribution in descending order.
Deletion removes top-ranked evidence for the target class.
Insertion restores top-ranked evidence for the target class.

## Run practical checks

* Confirm `predict_proba` returns at least two class columns.
* Confirm `model.classes_` contains the labels used to fit FPDE prototypes.
* Confirm every explained row has the same number of features as `X_train`.
* Check that attribution values are finite.
* Check that `details["target_label"]` and `details["rival_label"]` are different.
* Inspect `details["exactness_residual"]` for numerical stability.

## Avoid validation leakage

Use separate data splits for selection and final reporting.
For example, fit prototypes on training data, choose `lambda_hyb` on validation data, and report explanations on test data.

<Warning>
  Do not select `lambda_hyb` on the same examples you use for final claims.
  That makes the reported validation behavior less informative.
</Warning>

## Record enough context

Save the validation settings with the output:

* Fractions used in deletion and insertion curves
* Baseline vector source
* `lambda_hyb` or `lambda_hyb_grid`
* `normalize`
* `anchor_strategy`
* `eps`
* Model and preprocessing artifacts