Open sabinthomas opened 10 years ago
What you describe seems more in line with application code and it is beyond what a classifier is or does.
But working out confidence levels on the predictions is a direction ML packages are moving towards: http://scikit-learn.org/stable/modules/calibration.html
I'm retitling this and labeling a feature enhancement.
Classify() needs to implement a threshold mechanism for classify() errors. An error is a condition where the labels and probabilities are inconclusive, and a match cannot be obtained.
One way around this is by computing a priorProbabilities classification, and then comparing every getClassification result to the value of this priorProbabilities