Confusion matrix
Sensitivity (Recall / True Positive Rate): Number of positives out of all positives
- TP / (TP + FN)
- When you want to avoid false negatives
- % of people with a disease that are identified as having the disease
- "Recall all the positives in the dataset, how many did you get right?"
Specificity (True Negative rate): Correct positives out of the predicted positive results
- TN / (TN + FP)
- When you want to avoid false positives
- % of people without a disease that are identified as not having the disease
- "Recall all the negatives in the dataset, how many did you get right?"
Precision:
- TP / (TP + FP)
- How accurate are you?
- Out of all the things you said were positive, how many actually were?
Accuracy: Proportion of all predictions correctly identified
- (TP + TN) / Total
- Correct / total = Accuracy
- How correct overall am I?
ROC / AUC: Visualise the balance of sensitivity (TPR) and specificity (FPR) of a binary classifier when using different cutoff points for the classification
https://www.youtube.com/watch?v=4jRBRDbJemM
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GINI Impurity: Measure of impurity when evaluating which feature to use in splitting decision trees. The feature with the lowest GINI impurity score gets chosen as the root node.
F1 Score: Combination of Recall and precision, takes more into account FP and FN than accuracy.
- 2 * ( Recall * Precision) / ( Recall + Precision )