> ## 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.

# Quickstart

> Train a scikit-learn classifier and explain one prediction with FPDE

This tutorial trains a scikit-learn classifier, fits FPDE prototypes from the training data, and explains one test sample.
The example uses the breast cancer dataset bundled with scikit-learn.

## Prerequisites

* Python 3.12 or newer
* `fpde`
* A classifier that exposes `predict_proba` and `classes_`

<Steps>
  <Step title="Install FPDE">
    Install the stable package from PyPI.

    ```bash theme={null}
    python -m pip install fpde
    ```
  </Step>

  <Step title="Train a classifier">
    Train the model on the same feature space you will explain.

    ```python theme={null}
    from sklearn.datasets import load_breast_cancer
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler

    data = load_breast_cancer()
    X_train, X_test, y_train, _ = train_test_split(
        data.data,
        data.target,
        test_size=0.25,
        random_state=7,
        stratify=data.target,
    )

    scaler = StandardScaler()
    X_train = scaler.fit_transform(X_train)
    X_test = scaler.transform(X_test)

    model = LogisticRegression(max_iter=2000, random_state=7)
    model.fit(X_train, y_train)
    ```
  </Step>

  <Step title="Fit FPDE">
    Fit reusable class-mean prototypes from the training data.

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

    engine = FPDEEngine.fit(X_train, y_train, model=model)
    ```
  </Step>

  <Step title="Explain one sample">
    Compute a Hyb-FPDE explanation with a fixed `lambda_hyb`.

    ```python theme={null}
    import numpy as np

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

    print(np.asarray(attributions))
    print(details["target_label"], details["rival_label"], details["evidence"])
    ```
  </Step>
</Steps>

## Complete example

```python theme={null}
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

from fpde import FPDEEngine

data = load_breast_cancer()
X_train, X_test, y_train, _ = train_test_split(
    data.data,
    data.target,
    test_size=0.25,
    random_state=7,
    stratify=data.target,
)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

model = LogisticRegression(max_iter=2000, random_state=7)
model.fit(X_train, y_train)

engine = FPDEEngine.fit(X_train, y_train, model=model)
attributions, details = engine.explain_one(X_test[0], lambda_hyb=0.5)

print(np.asarray(attributions))
print(details["target_label"], details["rival_label"], details["evidence"])
```

## Interpret the output

`details["target_label"]` is the model's highest-probability class.
`details["rival_label"]` is the second-highest-probability class.
`details["evidence"]` is the target-versus-rival contrast decomposed by FPDE.

<Note>
  Positive attribution values support the target class.
  Negative attribution values support the rival class.
</Note>

## Next steps

* Use [Explain one sample](/explain-one) for a slower walkthrough of the result fields.
* Use [Explain batches](/explain-batches) when you need an attribution matrix.
* Use [Select lambda\_hyb](/select-lambda) to choose a fixed Hyb-FPDE mixture with validation data.