import numpy as np
from sklearn.metrics import accuracy_score

# Generate biased data with 80% of class 0 and 20% of class 1
y_true = np.concatenate((np.zeros(800), np.ones(200)))
y_pred = np.concatenate((np.zeros(700), np.ones(300)))

# Calculate overall accuracy
accuracy = accuracy_score(y_true, y_pred)

# Calculate accuracy per class
class_0_accuracy = accuracy_score(y_true[y_true == 0], y_pred[y_true == 0])
class_1_accuracy = accuracy_score(y_true[y_true == 1], y_pred[y_true == 1])

# Calculate inaccuracy
inaccuracy = 1 - ((class_0_accuracy * 0.8) + (class_1_accuracy * 0.2))

print(f"Overall accuracy: {accuracy}")
print(f"Class 0 accuracy: {class_0_accuracy}")
print(f"Class 1 accuracy: {class_1_accuracy}")
print(f"Inaccuracy: {inaccuracy}")