.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/regression_tree.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_regression_tree.py:


===============================================================
Fuzzy Regression Tree example
===============================================================

Example plot for :class:`FuzzyDecisionTreeRegressor` generated on synthetic data.

.. GENERATED FROM PYTHON SOURCE LINES 8-71

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn.metrics import mean_squared_error, r2_score
    from sklearn.model_selection import train_test_split

    from fuzzytree._classes import FuzzyDecisionTreeRegressor


    def generate_synthetic_data(n_samples=500, noise=0.1):
        """
        Generate synthetic regression data: y = sin(x) + noise
        """
        X = np.linspace(0, 10, n_samples).reshape(-1, 1)
        y = np.sin(X).ravel() + noise * np.random.randn(n_samples)
        return X, y


    def test_fuzzy_regression_tree():
        # Generate synthetic data
        X, y = generate_synthetic_data()

        # Split data into training and testing sets
        X_train, X_test, y_train, y_test = train_test_split(
            X, y, test_size=0.2, random_state=42
        )

        # Initialize the fuzzy regression tree
        regressor = FuzzyDecisionTreeRegressor(
            fuzziness=0.8,
            criterion="variance",  # Assuming 'variance' is implemented for regression
            max_depth=5,
            min_membership_split=0.02,
            min_membership_leaf=0.01,
            min_impurity_decrease=0.001,
        )

        # Fit the model
        regressor.fit(X_train, y_train)

        # Predict on the test set
        y_pred = regressor.predict(X_test)

        # Evaluate the model
        mse = mean_squared_error(y_test, y_pred)
        r2 = r2_score(y_test, y_pred)

        # Print the results
        print("Fuzzy Regression Tree Results:")
        print(f"Mean Squared Error (MSE): {mse:.4f}")
        print(f"R-squared (R2): {r2:.4f}")

        plt.scatter(X_test, y_test, color="blue", label="True Values")
        plt.scatter(X_test, y_pred, color="red", label="Predictions")
        plt.legend()
        plt.xlabel("X")
        plt.ylabel("y")
        plt.title("Fuzzy Regression Tree Performance")
        plt.show()


    if __name__ == "__main__":
        test_fuzzy_regression_tree()


.. _sphx_glr_download_auto_examples_regression_tree.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: regression_tree.ipynb <regression_tree.ipynb>`

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

      :download:`Download Python source code: regression_tree.py <regression_tree.py>`

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

      :download:`Download zipped: regression_tree.zip <regression_tree.zip>`


.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_