scikit-learn/examples/linear_model/plot_iris_logistic.py

# -*- coding: utf-8 -*-
"""
=========================================================
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
<https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ 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.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.inspection import DecisionBoundaryDisplay

# 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()