The Metrics
You can use the Confusion Matrix to compute metrics to associate with different needs.
Here's how to read the metrics.
Definition:
| Metrics | Definitions | Formula |
|---|---|---|
| Classification Rate | Proportion of targets accurately classified by the preditive model when applied on the validation data source. | (TP+TN)/N |
| Sensitivity | Proportion of actual positive targets that have been correctly predicted. | TP/(TP+FN) |
| Specificity | Proportion of actual negative targets that have been correctly predicted. | TN/(FP+TN) |
| Precision | Proportion of predictive positive targets that are actually positive targets. | TP/(TP+FP) |
| F1 score | Harmonic mean of Precision and Recall (Recall and Precision are evenly weighted). | 2 / ((1/Precision) + (1/Sensitivity)) |
| Fall-out | Proportion of negative targets that have been incorrectly detected as positive. | FP/(FP+TN) or (100% - Specificity) |
N = Number of observations
TP (True Positive) = Number of correctly predicted positive targets.
FN (False Negative) = Number of actual positive targets that have been predicted negative.
FP (False Positive) = Number of actual negative targets that have been predicted positive.
TN (True Negative) = Number of correctly predicted negative targets.