Strategies for accomodating
Strategies for accomodating - Greek sex roulete chat
decision trees with neural nets with SVMs and others) and also how the results of each independent model are combined.Ensembes may be categorized as either ‘selected’ or ‘fused’.
The second classifier is trained on data in which the first classifier achieved only 50% correct identification.
Winners edge out competitors at the fourth decimal place and like Formula 1 race cars, not many of us would mistake them for daily drivers.
The amount of time devoted and the sometimes extreme techniques wouldn’t be appropriate in a data science production environment, but like paddle shifters and exotic suspensions, some of those improvement find their way into day-to-day life.
The diagram below (Robi Polikar (2009), Scholarpedia, 4(1):2776) illustrates how the slightly different errors from each individual predictor are combined to create the best decision boundary.
There are many different strategies for ensuring diversity including combining different types of modeling algorithms (e.g.
In the ‘selected’ strategy the model which most accurately portrays a specific region of data is selected as the single predictor for that region and then combined with other similarly successful models that most accurately portray other specific areas of the data making up a patchwork of individual predictors.
The ‘fused’ strategy is more common and defines the dominant approach in bagging.Adaboost and GBM are quite similar except in the way they deal with their loss functions. Stacking (aka Stacked Generalization or Super Learner) may be the optimum ensemble technique but is also least understood (and most difficult to explain).Strengths: Bagging is not recommended on models that have a high bias. Conversely Boosting is not recommended for cases of high variance. Stacking creates an ensemble from a diverse group of strong learners.The third classifier is trained on data for which the first and second classifier disagree.The three classifiers are combined through a simple majority vote.One common way is to expose each classifier to different training data, most often through the use of bootstrap or jackknife resampling.