log_iris.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

# Load Iris dataset
iris = load_iris()
X, y = iris.data[:, :2], iris.target

# Split dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Fit logistic regression on training set
clf = LogisticRegression(solver='lbfgs', max_iter=1000, random_state=42)
clf.fit(X_train, y_train)

# Predict classes for the test set
y_pred = clf.predict(X_test)

# Compute accuracy of the model on test set
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy of the model: {accuracy}")

# Plot decision boundary
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
                     np.arange(y_min, y_max, 0.02))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.figure()
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.title('Logistic Regression Decision Boundary')
plt.show(