Evaluation metrics are crucial for assessing the performance of machine learning models. They help us understand how well our models are doing and where they might need improvement. In this tutorial, we will explore some common evaluation metrics used in machine learning.

Common Evaluation Metrics

  1. Accuracy: The simplest metric that calculates the proportion of correctly predicted instances to the total number of instances.
  2. Precision: The ratio of correctly predicted positive observations to the total predicted positives.
  3. Recall: The ratio of correctly predicted positive observations to the all observations in actual class.
  4. F1 Score: The weighted average of Precision and Recall, useful when the class distribution is uneven.
  5. Mean Squared Error (MSE): Measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value.
  6. Root Mean Squared Error (RMSE): The square root of the mean of the squares of the errors.

Practical Example

To understand these metrics better, let's consider a binary classification problem where we predict whether an email is spam or not.

  • Accuracy: If out of 100 emails, 90 are correctly classified as spam or not spam, the accuracy is 90%.
  • Precision: If out of the 10 emails predicted as spam, 8 are actually spam, the precision is 80%.
  • Recall: If out of the 20 spam emails, 8 are correctly predicted as spam, the recall is 40%.
  • F1 Score: The F1 Score for this example would be 0.5714 (using the formula: 2 * (Precision * Recall) / (Precision + Recall)).

Further Reading

For a more in-depth understanding of evaluation metrics, you can read our detailed guide on Machine Learning Metrics.

Accuracy Chart