Understanding Common Evaluation Metrics in Machine Learning
In the ever-evolving field of machine learning, evaluating the performance of models is crucial to ensuring their effectiveness and reliability. Different evaluation metrics serve different purposes, and understanding these metrics is key to selecting the best model for your specific application. In this article, we’ll explore some of the most common evaluation metrics used in machine learning, providing a clear and comprehensive guide to help you make informed decisions.
1. Accuracy
What It Measures: Accuracy is the proportion of correctly predicted instances out of the total instances.
Formula:
Use Case: Accuracy is most useful when the classes in the dataset are balanced, meaning there are roughly equal numbers of instances for each class.
Example: In a binary classification problem with 100 instances, if the model correctly predicts 90 instances, the accuracy is 90%.
2. Precision
What It Measures: Precision is the proportion of true positive predictions out of the total positive predictions (both true and false).
Formula:
Use Case: Precision is important in scenarios where the cost of false positives is high, such as in…
