ML11 min read

Model Evaluation Metrics: Choosing the Right One

Learn which metrics to use for different ML problems. Accuracy, precision, recall, F1 score, ROC-AUC - understand when to use each and why. Essential for evaluating models correctly.

Dr. Alex Kumar

December 18, 2025

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Choosing the right evaluation metric is crucial. Using the wrong metric can make a bad model look good or a good model look bad. Let's learn which metrics to use and when.

Classification Metrics

For classification, accuracy isn't always enough. Learn about precision (how many positives are actually correct), recall (how many actual positives you found), and F1 score (balance of both). Each tells you something different.

Regression Metrics

For regression, mean squared error, mean absolute error, and R-squared each have their place. I'll explain when to use which and what they actually mean for your model.

ROC-AUC and Confusion Matrix

ROC-AUC is great for binary classification, especially with imbalanced data. Confusion matrix shows you exactly where your model makes mistakes. Both are essential tools.

Real-World Examples

I'll show you real scenarios - when accuracy is misleading, when precision matters more than recall, and how to choose metrics based on your business goals. This is practical knowledge you'll use every day.

#ML#Evaluation Metrics#Accuracy#Precision#Recall

Common Questions & Answers

What's the difference between accuracy, precision, and recall?

Accuracy is overall correctness (correct predictions / total). Precision is how many predicted positives are actually positive (true positives / (true positives + false positives)). Recall is how many actual positives you found (true positives / (true positives + false negatives)). Use precision when false positives are costly, recall when false negatives are costly.

python

Run Explain

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Predictions
y_pred = model.predict(X_test)
y_true = y_test

# Calculate metrics
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)  # Precision
recall = recall_score(y_true, y_pred)        # Recall
f1 = f1_score(y_true, y_pred)               # F1 (harmonic mean)

print(f"Accuracy: {accuracy:.3f}")
print(f"Precision: {precision:.3f}")  # Of predicted positives, how many correct?
print(f"Recall: {recall:.3f}")        # Of actual positives, how many found?
print(f"F1: {f1:.3f}")                # Balance of precision and recall

When should I use ROC-AUC?

Use ROC-AUC for binary classification, especially with imbalanced datasets. It measures the model's ability to distinguish between classes across all thresholds. AUC of 1.0 is perfect, 0.5 is random. Good when you care about ranking predictions, not just binary classification.