predominance – MathStat Bites

January 24, 2023 by Moinak Bhaduri

Explainable groupings in the face of noisy, high-dimensional madness: Wild ambitions tamed through features’ salience

Whatever your exact interests in data, frequently, inseparable from model-building, stand other related responsibilities. Sample two crucial ones:

a. the checking of how well your model did: the less frequently you make big, bad decisions – like predicting someone’s salary to be $95, 000, a n e s t i m a t e f a r a d r i f t f r o m t h e r e a l, s a y,$ 70,000 in case it’s a regression problem, or saying a customer will buy a product when, in fact, she won’t, under a classification environment – the happier you are. These accuracies are unsurprisingly, often used to guide the model-building process.

b. the explaining of how you arrived at a prediction: this involves unpacking or interpreting the $95, 000. T h e p e r s o n, d u e t o h i s e x p e r i e n c e, m a k e s$ 10,000 more than the average, due to his education, makes $20, 000 m o r e, b u t d u e t o h i s s t a t e o f r e s i d e n c e, m a k e s$ 5000 less than the average, etc. These ups and downs contribute to a net final value.

contrasts feature salience high-dimensional clustering interpretable machine learning predominance recall unsupervised clustering

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