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Optimal Decision Boundaries

January 8, 2020
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Optimal Decision Boundaries

Introduction Over the next few posts, we will investigate decision boundaries. A decision boundary is a graphical representation of the solution to a classification problem. Decision boundaries can help us to understand what kind of solution might be appropriate for a problem. They can also help us to understand the how various machine learning classifiers arrive at a solution. In this...

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What I’m Reading 1: Bayes and Means

October 3, 2019
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What I’m Reading 1: Bayes and Means

Bayesian Aggregation Yang, Y., & Dunson, D. B., Minimax Optimal Bayesian Aggregation 2014 (arXiv) Say we have a number of estimators \(\hat f_1, \ldots, \hat f_K\) derived from a number of models \(M_1, \ldots, M_K\) for some regression problem \(Y = f(X) + \epsilon\), but, as is the nature of things when estimating with limited data, we don’t know which estimator...

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investmentsim – an R Package for Simulating Investment Portfolios

September 10, 2019
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investmentsim – an R Package for Simulating Investment Portfolios

I wrote a little package recently for a project I’ve been working on. I’ve mostly been using it to help out with Monte Carlo simulations for personal finance planning. It’s a little rough at the moment, but for the adventurous it’s on Github here: investmentsim. And here’s a quick tutorial on how to use it. The investmentsim package implements a...

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