This combination of models effectively reduces the variance in the “strong” model. (Outputs may be combined by several techniques for example, majority vote for classification and averaging for regression. These methods work by creating multiple diverse regression models, by taking different samples of the original dataset, and then combining their outputs. However, ensemble methods allow us to combine multiple “weak” regression models which, when taken together form a new, more accurate “strong” regression model. We can view the statistics and confusion matrices of the current predictor to see if our model is a good fit to the data, but how would we know if there is a better predictor just waiting to be found? The answer is that we do not know if a better predictor exists. ![]() Analytic Solver Data Mining Regression Algorithms on their own can be used to find one model that results in good predictions for the new data. ![]() Analytic Solver Data Mining offers three powerful ensemble methods for use with Regression: bagging (bootstrap aggregating), boosting, and random trees.
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