Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

Note

Go to the end to download the full example code or to run this example in your browser via Binder

# Logistic Regression 3-class Classifier

Show below is a logistic-regression classifiers decision boundaries on the first two dimensions (sepal length and width) of the iris dataset. The datapoints are colored according to their labels.

    # Code source: Gaël Varoquaux
    # Modified for documentation by Jaques Grobler
    # License: BSD 3 clause

    import matplotlib.pyplot as plt

    from sklearn import datasets
    from sklearn.inspection import DecisionBoundaryDisplay
    from sklearn.linear_model import LogisticRegression

    # import some data to play with
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features.
    Y = iris.target

    # Create an instance of Logistic Regression Classifier and fit the data.
    logreg = LogisticRegression(C=1e5)
    logreg.fit(X, Y)

    _, ax = plt.subplots(figsize=(4, 3))
    DecisionBoundaryDisplay.from_estimator(
        logreg,
        X,
        cmap=plt.cm.Paired,
        ax=ax,
        response_method="predict",
        plot_method="pcolormesh",
        shading="auto",
        xlabel="Sepal length",
        ylabel="Sepal width",
        eps=0.5,
    )

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors="k", cmap=plt.cm.Paired)

    plt.xticks(())
    plt.yticks(())

    plt.show()

Total running time of the script: (0 minutes 0.033 seconds)

Gallery generated by Sphinx-Gallery