If a classification system has been trained a confusion matrix will summarize the results (ie the error rate (false|true) (positive|negative) for a binary classification).
This is training error, there is may be overfitting.
The main diagonal indicates correct classification whereas everything off the main diagonal indicates a classification.
- On the main diagonals is where we do correct (ie true) classification (True Positive and True Negative)
- On the off diagonals is where we make mistakes (ie false) (False Positive and False Negative)
|Yes||True Positive||False Negative|
|No||False Positive||True Negative|
Titanic Data Set
With a simple rule: If most females survived, then assume every female survives
Survived: Yes or No
- Total of good predictions : 233 + 468 = 701
- Total of bad predictions : 81 + 109 = 190
- Percentage of good prediction: 701 /( 701 + 190)*100 = 79%
Documentation / Reference
- Bill Howe - Data Science Course