Machine learning metrics are crucial for evaluating the performance of algorithms. Here's a comparison of some commonly used metrics in machine learning:
Accuracy: This is the ratio of correctly predicted observations to the total observations. It's simple to understand but doesn't consider the imbalance in class distribution.
Precision: Precision is the ratio of true positives to the sum of true and false positives. It's useful when the cost of a false positive is high.
Recall: Recall, also known as sensitivity, is the ratio of true positives to the sum of true positives and false negatives. It's important when the cost of a false negative is high.
F1 Score: The F1 score is the harmonic mean of precision and recall. It provides a balance between precision and recall, making it a good metric for imbalanced datasets.
For more information on machine learning metrics and their applications, you can visit our Machine Learning Basics page.