Is your Classification Model making lucky guesses?

by Shaheen Gauher, PhD, Data Scientist at Microsoft At the heart of a classification model is the ability to assign a class to an object based on its description or features. When we build a classification model, often we have to prove that the model we built is significantly better than random guessing. How do we know if our machine learning model performs better than a classifier built by assigning labels or classes arbitrarily (through random guess, weighted guess etc.)? I will call the latter non-machine learning classifiers as these do not learn from the data. A machine learning classifier...

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