accuracy recall precision

[[Accuracy]] = $$\frac{TP+TN}{TP+TN+FP+FN}$$

[[Recall]] = $$\frac{TP}{TP+FN}$$

[[Precision]] = $$\frac{TP}{TP+FP}$$ [[False positive rate]] = $$\frac{FP}{FP+TN}$$

[[Accuracy]]

• Use as a rough indicator of model training progress/convergence for balanced datasets.
• For model performance, use only in combination with other metrics.
• Avoid for imbalanced datasets. Consider using another metric.

[[Recall]] - (True positive rate)

• Use when false negatives are more expensive than false positives.

[[False Positive Rate]]

• Use when false positives are more expensive than false negatives.

[[Precision]]

• Use when it’s very important for positive predictions to be accurate.