In predictive modeling, bagging is an ensemble method that uses bootstrap replicates of the original training data to fit predictive models. For each record, the predictions from all available models are then averaged for the final prediction. For a classification problem, a majority vote of the models is used. Bagging is short for “bootstrap aggregating.”
This course introduces to the basic concepts in predictive analytics to visualize and explore data to understand the two core paradigms that account for most business applications of predictive modeling: classification and prediction.