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.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/05_explain_prediction.py"
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.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_05_explain_prediction.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_05_explain_prediction.py:


Explaining one prediction
=========================

Ask a soft decision tree why it predicted what it predicted for a single
sample: which leaf got the mass, how each gate on the way leaned and why, and
what single change would have flipped the class.

Run with:
    python examples/05_explain_prediction.py

.. GENERATED FROM PYTHON SOURCE LINES 12-33




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    --- test sample 0, true class 0 ---
    predicted 0 with probability 0.825
    dominant leaf 9 received 0.999 of the sample's mass; its distribution is 0: 0.825, 1: 0.089, 2: 0.086
    path:
      gate 0: went left with p=1.000  (-2.503[proline] -1.391[alcohol] -1.314[magnesium])
      gate 1: went right with p=1.000  (+2.603[proline] +1.511[ash] +1.214[alcohol])
      gate 4: went left with p=1.000  (-3.697[proline] -1.693[alcohol] -1.222[alcalinity_of_ash])
    largest contributions: proline 8.800, alcohol 4.296, ash 2.832, alcalinity_of_ash 2.604, flavanoids 1.750
    counterfactual: set proline from 1.906 to -3.025 and the prediction becomes 1 (p=0.482)

    --- test sample 1, true class 1 ---
    predicted 1 with probability 0.836
    dominant leaf 14 received 0.969 of the sample's mass; its distribution is 0: 0.082, 1: 0.840, 2: 0.078
    path:
      gate 0: went right with p=0.972  (+1.632[alcohol] +1.614[proline] -1.386[hue])
      gate 2: went right with p=0.998  (+3.320[hue] +1.558[alcohol] +1.014[color_intensity])
      gate 6: went right with p=0.999  (+2.082[proline] +1.446[hue] +1.215[alcohol])
    largest contributions: hue 6.014, alcohol 4.323, proline 4.266, color_intensity 1.873, malic_acid 1.668
    counterfactual: set proline from -1.229 to 2.787 and the prediction becomes 0 (p=0.543)

    --- test sample 2, true class 0 ---
    predicted 0 with probability 0.824
    dominant leaf 9 received 0.999 of the sample's mass; its distribution is 0: 0.825, 1: 0.089, 2: 0.086
    path:
      gate 0: went left with p=1.000  (-2.563[alcohol] -2.160[proline] -2.084[alcalinity_of_ash])
      gate 1: went right with p=1.000  (+2.247[proline] +2.235[alcohol] +0.881[od280/od315_of_diluted_wines])
      gate 4: went left with p=1.000  (-3.190[proline] -3.118[alcohol] -2.353[alcalinity_of_ash])
    largest contributions: alcohol 7.913, proline 7.594, alcalinity_of_ash 5.013, od280/od315_of_diluted_wines 2.674, flavanoids 1.099
    counterfactual: set alcohol from 1.647 to -3.315 and the prediction becomes 1 (p=0.606)







|

.. code-block:: Python

    from sklearn.datasets import load_wine
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler

    from neural_trees import SoftDecisionTree

    data = load_wine()
    X_train, X_test, y_train, y_test = train_test_split(
        data.data, data.target, test_size=0.3, stratify=data.target, random_state=0
    )
    scaler = StandardScaler().fit(X_train)
    model = SoftDecisionTree(depth=3, max_epochs=60, random_state=0)
    model.fit(scaler.transform(X_train), y_train)

    # Explain the first three test samples. Values are in standardised units,
    # because that is what the model was fitted on.
    explanations = model.explain(scaler.transform(X_test[:3]), feature_names=list(data.feature_names))
    for i, e in enumerate(explanations):
        print(f"--- test sample {i}, true class {y_test[i]} ---")
        print(e.to_text())
        print()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.187 seconds)


.. _sphx_glr_download_auto_examples_05_explain_prediction.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 05_explain_prediction.ipynb <05_explain_prediction.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: 05_explain_prediction.py <05_explain_prediction.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: 05_explain_prediction.zip <05_explain_prediction.zip>`


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 .. rst-class:: sphx-glr-signature

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