# Metrics in ML problems

Published on April 8, 2023

## Classification

### Accuracy

Accuracy measures the percentage of correctly predicted labels in the test set. While it is a useful metric, it may not always be the best indicator of a model's performance, especially when the classes are imbalanced.

### Precision

Precision measures the percentage of true positives among all positive predictions. It is a useful metric when the goal is to minimize false positives.

### Recall

Recall measures the percentage of true positives among all actual positives. It is a useful metric when the goal is to minimize false negatives.

### F1 Score

F1 Score is the harmonic mean of precision and recall. It is a useful metric when you want to balance the importance of precision and recall.

### Area Under the ROC Curve

The ROC curve is a graphical representation of the trade-off between sensitivity and specificity. AUC measures the area under the ROC curve and is a useful metric when the goal is to balance sensitivity and specificity.

## Regression

### Mean Squared Error

MSE measures the average squared difference between the predicted and actual values. It is a commonly used metric in regression problems.

### Root Mean Squared Error

RMSE is the square root of MSE and measures the average difference between the predicted and actual values. It is a useful metric when you want to express the error in the same units as the target variable.

### Mean Absolute Error

MAE measures the average absolute difference between the predicted and actual values. It is a useful metric when you want to avoid the influence of outliers.