739 research outputs found
Optimization by gradient boosting
Gradient boosting is a state-of-the-art prediction technique that
sequentially produces a model in the form of linear combinations of simple
predictors---typically decision trees---by solving an infinite-dimensional
convex optimization problem. We provide in the present paper a thorough
analysis of two widespread versions of gradient boosting, and introduce a
general framework for studying these algorithms from the point of view of
functional optimization. We prove their convergence as the number of iterations
tends to infinity and highlight the importance of having a strongly convex risk
functional to minimize. We also present a reasonable statistical context
ensuring consistency properties of the boosting predictors as the sample size
grows. In our approach, the optimization procedures are run forever (that is,
without resorting to an early stopping strategy), and statistical
regularization is basically achieved via an appropriate penalization of
the loss and strong convexity arguments
Econometrics of Machine Learning Methods in Economic Forecasting
This paper surveys the recent advances in machine learning method for
economic forecasting. The survey covers the following topics: nowcasting,
textual data, panel and tensor data, high-dimensional Granger causality tests,
time series cross-validation, classification with economic losses
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