Machine learning metrics are crucial for evaluating the performance of machine learning models. In this tutorial, we will discuss some common metrics used in machine learning.

Common Metrics

Here are some of the most commonly used metrics in machine learning:

  • Accuracy: This metric calculates the number of correct predictions made by the model over the total number of predictions.
    • Formula: Accuracy = (Number of Correct Predictions) / (Total Number of Predictions)
  • Precision: Precision measures the proportion of positive identifications that were actually correct.
    • Formula: Precision = (True Positives) / (True Positives + False Positives)
  • Recall: Recall measures the proportion of actual positives that were identified correctly.
    • Formula: Recall = (True Positives) / (True Positives + False Negatives)
  • F1 Score: The F1 score is the harmonic mean of precision and recall. It is a balanced measure that considers both false positives and false negatives.
    • Formula: F1 Score = 2 * (Precision * Recall) / (Precision + Recall)

Visualization

Machine Learning Metrics

To learn more about machine learning metrics and other concepts, check out our Machine Learning Basics tutorial.