Welcome to the Machine Learning Metrics Guide! This resource will help you understand key evaluation metrics for ML models. Let's dive into the essentials:

📈 Common Metrics for Classification

  • Accuracy
    Measures the ratio of correctly predicted instances.

    Accuracy
    [Learn more about accuracy](/en/resources/ml/model-evaluation)

  • Precision & Recall

    • Precision: True Positives / (True Positives + False Positives)
    • Recall: True Positives / (True Positives + False Negatives)
    Precision Recall Curve

  • F1 Score
    Harmonic mean of precision and recall. Ideal for imbalanced datasets.
    Explore F1 optimization techniques

📊 Regression Metrics

  • Mean Absolute Error (MAE)
    Average absolute difference between predicted and actual values.

    MAE Regression

  • R² Score
    Explains the proportion of variance in the dependent variable.
    Compare MAE vs R²

🔄 Metric Selection Tips

  • Use accuracy for balanced datasets
  • Prefer F1 score when dealing with class imbalance
  • Always visualize metrics with confusion matrices
  • 🚀 For deep learning, track AUC-ROC curves for binary classification

Need help choosing the right metric for your project? Check our decision framework for actionable insights!