Random Forest for churn prediction

As described in Chapter 1, Analyzing Insurance Severity Claim, Random Forest is an ensemble technique that takes a subset of observations and a subset of variables to build decision trees—that is, an ensemble of DTs. More technically, it builds several decision trees and integrates them together to get a more accurate and stable prediction.

Figure 7: Random forest and its assembling technique explained  

This is a direct consequence, since by maximum voting from a panel of independent juries, we get the final prediction better than the best jury (see the preceding figure). Now that we already know the working ...

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