📊 Choosing the Right Scoring Metric in Cross-Validation

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When training a machine learning model, evaluating it properly is just as important as building it. In Python’s scikit-learn, the function cross_val_score() is widely used for cross-validation. But a common confusion arises: Which scoring metric should we use?

🔑 Key Idea: Metric Depends on Problem Type

If you use a regression metric for a classification model, it will either fail or give meaningless results.

✅ Correct Metric for Classification

In the screenshot, the question asks:

Which of the following scoring metrics could be used for evaluating a classification model in cross-validation using cross_val_score()?

Correct Answer → roc_auc

Why?

  • roc_auc (Receiver Operating Characteristic - Area Under Curve) measures how well the model separates classes.

  • Other options like r2, mean_absolute_error, and explained_variance are regression metrics — not suitable for classification.

🐍 Python Example

Let’s see it in action with scikit-learn:

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression

# Load a classification dataset
X, y = load_breast_cancer(return_X_y=True)

# Define model
model = LogisticRegression(max_iter=500)

# Evaluate with cross-validation using ROC-AUC
scores = cross_val_score(model, X, y, cv=5, scoring="roc_auc")

print("ROC-AUC Scores:", scores)
print("Average ROC-AUC:", scores.mean())

Output (example):

ROC-AUC Scores: [0.98 0.99 0.97 0.98 0.99]
Average ROC-AUC: 0.982

📌 Takeaway

  • Always match the scoring metric to your problem type.

  • For classification, use accuracy, f1, precision, recall, or roc_auc.

  • For regression, use r2, neg_mean_squared_error, neg_mean_absolute_error, etc.

  • Using the wrong metric (like r2 for classification) will lead to incorrect evaluation.

🚀 Next time you use cross_val_score(), remember: ROC-AUC is perfect for binary classification evaluation.

Do you want me to extend this blog into a comparison table of metrics (classification vs regression) so it’s easier for beginners to quickly pick the right one?

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