Battery: Priors Part 1
In this video, we discuss prior probabilities in CART (Classification and Regression Trees). In a binary classification problem we are trying to separate records into two classes (i.e. fraud, no fraud; spam, no spam). There are certain decisions that the algorithm must make in order to properly classify each record when building the decision tree. The machinery of prior probabilities allows the analyst to directly influence these decisions and influence critical internal decisions as the algorithms is trying to balance the tree it builds and avoid false positive classification. We review how this works in CART in a manual way, and in the next video we will how you can improve your classification results using Battery PRIORS.
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