# Deep Learning

## Accuracy, precision and recall

In deep learning object class predictions are often characterised by the following accuracy metrics.

- Accuracy - ratio of correct classifications i.e. "how correct predictions are overall"
- Precision - ratio of correct identified positive out of predicted positive i.e. "how correct prediction are of positive"
- Recall - ratio of correct predicted positive out of actual positive i.e. "how many of actual positive are found"

Measures are calculated as follows.

```
Predicted
Postive Negative
Actual / Positive TP FN
Ground Truth Negative FP TN
```

- Accuracy = Accurately predicted / All = TP + TN / All
- Precision = True positive / Predicted Postive = TP / (TP + FP)
- Recall = True positive / Actul Positive = TP / (TP + FN)