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[Discussion] How to interpret probabilities?

So I’ve worked with machine learning a lot, but never actually had to deal directly with probabilities/probability output, and what they actually mean in a real world scenario.

So, say we have 4 balanced classes [1,2,3, and 4], and we train a Keras classifier and get the probabilities for a given prediction in the test set:

GT: Class 1

Output: [0.4, 0.3, 0.1, 0.2]

Great – the classifier predicted Class 1 correctly. But how should we interpret the 0.4 probability that was attached to the class?

Does it mean that given that particular feature vector, class 1 will be the correct choice 40% of the time?

Where does the actual performance/accuracy of the classifier come in to this?

For example, say the above classifier was trained with 100k training examples, had an accuracy of ‘50%’, and produced the probabilities of [0.4, 0.3, 0.1, 0.2]. Now imagine I got more training data and trained with 500k training examples, achieved an accuracy of 60%, and still got the probabilities of [0.4, 0.3, 0.1, 0.2]. Does the more accurate classifier better represent the actual real-life probability of the events?

tl;dr: I guess what I’m actually asking is – how do we know/measure/compare the accuracy of the probabilities given from a classifier?

submitted by /u/Zman420
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.