Machine learning evaluation is a crucial step in the machine learning process. It involves assessing the performance of a model on a given task. This section provides an overview of the key evaluation metrics and techniques used in machine learning.

Common Evaluation Metrics

Here are some common evaluation metrics used for various machine learning tasks:

  • Accuracy: The proportion of correct predictions out of all predictions made.
  • Precision: The proportion of true positives among all positive predictions.
  • Recall: The proportion of true positives out of all actual positives.
  • F1 Score: The harmonic mean of precision and recall, used to balance the two metrics.

Evaluation Techniques

There are several techniques to evaluate machine learning models:

  • Cross-Validation: This technique involves splitting the data into multiple subsets and training and testing the model on each subset. The results are then averaged to get a better estimate of the model's performance.
  • Hold-Out Validation: This technique involves splitting the data into training and test sets and then evaluating the model on the test set.

Cross-Validation Diagram

For more detailed information on evaluation techniques, you can visit our evaluation techniques page.

Performance Metrics for Classification

When working with classification tasks, several metrics can be used to evaluate model performance:

  • Confusion Matrix: This matrix shows the number of correct and incorrect predictions for each class.
  • ROC Curve: This curve plots the true positive rate against the false positive rate at various threshold settings.

Confusion Matrix

To learn more about performance metrics for classification, check out our classification metrics page.