10_2.py

# 5_2 Accounting for unequal error costs using python
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report

data = np.array([[1], [2], [3], [4], [5], [6]])
target = np.array([0, 0, 0, 1, 1, 1])

X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)

model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)

print("Confusion Matrix:")
print(conf_matrix)
print("\nClassification Report:")
print(class_report)