Understand the core classification metrics and when to prefer each—especially on imbalanced datasets.
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
y_true = [1,0,1,1,0,0,1,0]
y_pred = [1,0,1,0,0,0,1,1]
print("Accuracy:", accuracy_score(y_true, y_pred))
print("Precision:", precision_score(y_true, y_pred))
print("Recall:", recall_score(y_true, y_pred))
print("F1:", f1_score(y_true, y_pred))