Tag Archives: Machine Learning

Cross-Fitting Double Machine Learning estimator

By Gabriel Vasconcelos Motivation In a late post I talked about inference after model selection showing that a simple double selection procedure is enough to solve the problem. In this post I’m going to talk about a generalization of the … Continue reading

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Bagging, the perfect solution for model instability

By Gabriel Vasconcelos Motivation The name bagging comes from boostrap aggregating. It is a machine learning technique proposed by Breiman (1996) to increase stability in potentially unstable estimators. For example, suppose you want to run a regression with a few … Continue reading

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