Overview
Model evaluation metrics are essential tools in machine learning that help assess how well a model performs. By using metrics like accuracy, precision, recall, and F1 score, practitioners can gain insights into the strengths and weaknesses of their models. Understanding these metrics allows for bett...
Key Terms
Example: If a model predicts 80 out of 100 instances correctly, its accuracy is 80%.
Example: If a model predicts 10 positives and 8 are correct, precision is 80%.
Example: If there are 10 actual positives and the model identifies 8, recall is 80%.
Example: An F1 score of 0.8 indicates a good balance between precision and recall.
Example: If a model correctly identifies 7 spam emails, TP = 7.
Example: If a model incorrectly identifies 3 non-spam emails as spam, FP = 3.