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Your Lopsided Model is Out to Get You

For classification problems I argue one of the biggest steps you can take to improve the quality and utility of your models is to prefer models that return scores or return probabilities instead of classification rules. Doing this also opens a second large opportunity for improvement: working with your domain […]

Estimating Uncertainty of Utility Curves

Recently, we showed how to use utility estimates to pick good classifier thresholds. In that article, we used model performance on an evaluation set, combined with estimates of rewards and penalties for correct and incorrect classifications, to find a threshold that optimized model utility. In this article, we will show […]

The Geometry of Classifiers

As John mentioned in his last post, we have been quite interested in the recent study by Fernandez-Delgado, et.al., “Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?” (the “DWN study” for short), which evaluated 179 popular implementations of common classification algorithms over 120 or so data […]

Can a classifier that never says “yes” be useful?

Many data science projects and presentations are needlessly derailed by not having set shared business relevant quantitative expectations early on (for some advice see Setting expectations in data science projects). One of the most common issues is the common layman expectation of “perfect prediction” from classification projects. It is important […]

More on ROC/AUC

A bit more on the ROC/AUC The issue The receiver operating characteristic curve (or ROC) is one of the standard methods to evaluate a scoring system. Nina Zumel has described its application, but I would like to call out some additional details. In my opinion while the ROC is a […]