Tag Archives: Machine Learning

How Random Forests improve simple Regression Trees?

By Gabriel Vasconcelos Regression Trees In this post I am going to discuss some features of Regression Trees an Random Forests. Regression Trees are know to be very unstable, in other words, a small change in your data may drastically … Continue reading

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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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