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.

plot iris logistic
# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# SPDX-License-Identifier: 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 0.049 seconds)

Related examples

The Iris Dataset

The Iris Dataset

SVM with custom kernel

SVM with custom kernel

Plot different SVM classifiers in the iris dataset

Plot different SVM classifiers in the iris dataset

PCA example with Iris Data-set

PCA example with Iris Data-set

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