Understanding accuracy, precision, recall, F1-score, and MAE.
To evaluate a model, we need quantitative measures of its performance, known as performance metrics. The choice of metric depends heavily on the type of problem (regression or classification) and the specific goals of the project. For classification problems, Accuracy is the most intuitive metric. It's simply the ratio of correct predictions to the total number of predictions. However, accuracy can be misleading, especially for imbalanced datasets (where one class is much more frequent than others). In such cases, other metrics are more informative. Precision measures the accuracy of positive predictions: out of all the predictions for the positive class, how many were correct? Recall (or Sensitivity) measures how well the model finds all the positive instances: out of all the actual positive instances, how many did the model correctly identify? The F1-Score is the harmonic mean of precision and recall, providing a single metric that balances both. For regression problems, where we predict continuous values, we use different metrics. Mean Absolute Error (MAE) is the average of the absolute differences between the predicted and actual values. Root Mean Squared Error (RMSE) is the square root of the average of the squared differences. It penalizes larger errors more heavily than MAE. Understanding which metric to use and how to interpret it is crucial for judging the true performance of a model.