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.
Ensemble modelling refers to the practice of combining the predictions of separate models on the old principle that “two heads are better than one”. Ensemble methods can be particularly effective when combining models that have been created using completely different algorithms. As each algorithm has its own unique set of strengths and weaknesses, it’s not surprising that there may be certain data rows that algorithm A is better at classifying than Algorithm B and vice versa. By combining the resultant models to create a single ensemble model, we often find that the overall accuracy of the ensemble method is better than any one individual contributing model.
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