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How to Build Ensemble Models in Machine Learning

Ensemble methods signifies the use of techniques that create numerous models, which are then brought together for the sole purpose of improving the accuracy of results. Ensemble models are preferred because they can forecast results that are more accurate than when a single model is used. If you have been following recent machine learning competitions, then you might have noticed that the eventual winners mostly use ensemble models. The combined models or algorithms are called base learners, and the resultant system is able to incorporate all the predictions from every algorithm used in the base learners. These models work, for example, in the case of an interview, in which every interviewer has the opportunity to assess the qualities of the candidate. The final decision will not be made by one of the interviewers, but rather by all of them based on how they assessed the candidate. Below we cover ways in which you can build ensemble models in machine learning.

1. Bagging

Bagging is also known as bootstrap aggression. In order to understand how bagging works, you must first comprehend bootstrapping. Bootstrapping is a method of sampling in which we choose a number of rows (n) from the original dataset of the (n) rows. This implies that there is a likelihood of equally selecting each row in every iteration. Every row in the dataset in this case has the same probability of being selected. The moment you obtain the bootstrapped samples, then you can use either majority vote or averaging concepts to obtain final decisions. Bagging works this way, too. Bagging also helps to bring down cases of variances. For a business, making accurate market predictions can steer you well ahead of your competition. You can hire data engineers from sites like Active Wizards to help you correctly analyze your data before making your final predictions.

2. Boosting

This is a technique that uses sequences. The algorithm used in the beginning is trained on the whole dataset, and then all the algorithms that follow are created by fitting the residuals of the algorithm that was used at the beginning. This will give greater weight to the observations that were not accurately predicted by the previous model. Boosting depends entirely on the building of a series of weak base learners, which may not be good for the whole dataset, but rather just part of it. At the end, though, each of the models ends up boosting the performance of the whole ensemble. Boosting is aimed at reducing bias that can arise from predictions made by a single model. As such, boosted algorithms can sometimes be vulnerable to over-fitting. You can use parameter tuning to reduce cases of over-fitting algorithms.

3. Stacking

With stacking, numerous machine learning models are placed in layers, one over the other, and when one model makes a prediction, it sends the prediction to the model above it. The model placed at the top will make the final decision, depending on the output of the other models below it. Each model makes its own prediction, but the top one chooses the most accurate one. The top model can also be substituted by methods like averaging, majority vote, or weighted average.


Published Wednesday, January 30, 2019 9:40 AM by David Marshall
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