November 2020
Beginner to intermediate
352 pages
8h 43m
English

In the previous chapter, we helped you understand how to make predictions using linear and logistic regression—two workhorses of modern data analytics—and how to measure the quality of a prediction using metrics like root mean square error (RMSE) and area under the curve (AUC). Companies across sectors have used these types of predictive models for decades.
So what’s new today? Why is there so much buzz around predictive analytics? The answer is machine learning (ML), a new class of models that often yield better predictions because of the highly flexible way in which they use input variables to predict an outcome. ...
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