Blog Archives

Handling Class Imbalance with R and Caret – Caveats when using the AUC

January 2, 2017
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Handling Class Imbalance with R and Caret – Caveats when using the AUC

In my last post, I went over how weighting and sampling methods can help to improve predictive performance in the case of imbalanced classes. I also included an applied example with a simulated dataset that used the area under the ROC curve (AUC) as th...

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Handling Class Imbalance with R and Caret – An Introduction

December 9, 2016
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Handling Class Imbalance with R and Caret – An Introduction

When faced with classification tasks in the real world, it can be challenging to deal with an outcome where one class heavily outweighs the other (a.k.a., imbalanced classes). The following will be a two-part post on some of the techniques that can hel...

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Clustering Mixed Data Types in R

June 21, 2016
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Clustering Mixed Data Types in R

Clustering allows us to better understand how a sample might be comprised of distinct subgroups given a set of variables. While many introductions to cluster analysis typically review a simple application using continuous variables, clustering data of mixed types (e.g., continuous, ordinal, and nominal) is often of interest. The following is an overview of one approach to clustering data...

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Partial Dependence Plots

December 22, 2014
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Partial Dependence Plots

It can be difficult to understand the functional relations between predictors and an outcome when using black box prediction methods like random forests. One way to investigate these relations is with partial dependence plots. These plots are graphical visualizations of the marginal effect of a given variable (or multiple variables) on an outcome. Typically, these are restricted to only...

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