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"""
=========================================================
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 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()
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