Logistic Regression Code:
file name : logistic_regression.py
import numpy as np
from sklearn import
linear_model
import matplotlib.pyplot
as plt
from utilities import
visualize_classifier
# Define sample input data
X = np.array([[3.1, 7.2],
[4, 6.7], [2.9, 8], [5.1, 4.5], [6, 5], [5.6, 5], [3.3, 0.4], [3.9, 0.9],
[2.8, 1], [0.5, 3.4], [1, 4], [0.6, 4.9]])
y = np.array([0, 0, 0, 1,
1, 1, 2, 2, 2, 3, 3, 3])
# Create the logistic
regression classifier
#classifier = linear_model.LogisticRegression(solver='liblinear',
C=1)
classifier =
linear_model.LogisticRegression(solver='liblinear', C=100)
# Train the classifier
classifier.fit(X, y)
# Visualize the
performance of the classifier
visualize_classifier(classifier,
X, y)
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