![]() The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data. This approach for construction of a super learner generalizes to any parameter which can be defined as a minimizer of a loss function.ĭespite significant successes achieved in knowledge discovery, traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data, such as imbalanced, high-dimensional, noisy data, etc. In addition, this paper contains a practical demonstration of the adaptivity of this so called super learner to various true data generating distributions. ![]() ![]() This article proposes a fast algorithm for constructing a super learner in prediction which uses V-fold cross-validation to select weights to combine an initial set of candidate learners. Motivated by this use of cross validation, we propose a new prediction method for creating a weighted combination of many candidate learners to build the super learner. (2007)) theoretically validated the use of cross validation to select an optimal learner among many candidate learners. Previous articles (van der Laan and Dudoit (2003) van der Laan et al. A few examples of these candidate learners are: least squares, least angle regression, random forests, and spline regression. ![]() When trying to learn a model for the prediction of an outcome given a set of covariates, a statistician has many estimation procedures in their toolbox. ![]()
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