Tag Archives: unsupervised clustering

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, an estimate far adrift from the real, say, $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. The person, due to his experience, makes $10,000 more than the average, due to his education, makes $20,000 more, but due to his state of residence, makes $5000 less than the average, etc. These ups and downs contribute to a net final value.

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