Machine Learning Metrics

Key Evaluation Metrics in Machine Learning

Here are the most commonly used metrics to assess model performance:

1. Accuracy
   Measures the ratio of correct predictions to total predictions.

   [Learn more about accuracy](/en/tech/machine-learning/accuracy)

2. Precision
   Reflects the proportion of true positive predictions among all positive predictions.

   

3. Recall
   Indicates the ability to capture all actual positive instances.

   

4. F1 Score
   Balances precision and recall via harmonic mean.

   

5. ROC Curve
   Visualizes trade-offs between true positive rate and false positive rate.

   

6. Confusion Matrix
   A table showing actual vs predicted class distributions.

   

For deeper understanding, explore our Machine Learning Tutorial series. 📊🤖
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