Many analysts who are interested in building predictive models invest a lot of their time and effort in trying to understand how to best tune the parameters of the specific technique that they are using, whether that technique be logistic regression or a neural network, and they are doing this in order to achieve the best accuracy of the resultant model. In this series of videos we look at some often overlooked approaches that can be applied in the same way to a wide variety of algorithms and which may lead to better predictive accuracy. In all of our examples we’ll focus on improving the accuracy of a predictive model applied to a classification prediction problem.
Split models
Split models or split population modelling is another technique that allows the user to build multiple models which can then be combined to create a single prediction. The idea with split modelling is that if the data represent different populations or contain separate groups that behave in very different ways, assuming that a single model can explain all the inherent variability across these distinct populations might be unreasonable. In which case, why not build separate local models for these key segments in the data and aggregate the resultant scores with the aim of increasing overall accuracy.
Watch this video to find out more
Check out the other videos in this series
- 6 secrets of building better models part one: bootstrap aggregation
- 6 secrets of building better models part two: boosting
- 6 secrets of building better models part three: feature engineering
- 6 secrets of building better models part four: ensemble modelling
- 6 secrets of building better models part five: meta models
- 6 secrets of building better models part six: split models
