# Plot multinomial and One-vs-Rest Logistic Regression¶

Plot decision surface of multinomial and One-vs-Rest Logistic Regression. The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers are represented by the dashed lines.

training score : 0.995 (multinomial)
training score : 0.976 (ovr)


# Authors: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
# License: BSD 3 clause

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.linear_model import LogisticRegression
from sklearn.inspection import DecisionBoundaryDisplay

# make 3-class dataset for classification
centers = [[-5, 0], [0, 1.5], [5, -1]]
X, y = make_blobs(n_samples=1000, centers=centers, random_state=40)
transformation = [[0.4, 0.2], [-0.4, 1.2]]
X = np.dot(X, transformation)

for multi_class in ("multinomial", "ovr"):
clf = LogisticRegression(
solver="sag", max_iter=100, random_state=42, multi_class=multi_class
).fit(X, y)

# print the training scores
print("training score : %.3f (%s)" % (clf.score(X, y), multi_class))

_, ax = plt.subplots()
DecisionBoundaryDisplay.from_estimator(
clf, X, response_method="predict", cmap=plt.cm.Paired, ax=ax
)
plt.title("Decision surface of LogisticRegression (%s)" % multi_class)
plt.axis("tight")

# Plot also the training points
colors = "bry"
for i, color in zip(clf.classes_, colors):
idx = np.where(y == i)
plt.scatter(
X[idx, 0], X[idx, 1], c=color, cmap=plt.cm.Paired, edgecolor="black", s=20
)

# Plot the three one-against-all classifiers
xmin, xmax = plt.xlim()
ymin, ymax = plt.ylim()
coef = clf.coef_
intercept = clf.intercept_

def plot_hyperplane(c, color):
def line(x0):
return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]

plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color)

for i, color in zip(clf.classes_, colors):
plot_hyperplane(i, color)

plt.show()


Total running time of the script: ( 0 minutes 0.173 seconds)

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