Posted on Categories Practical Data Science, Pragmatic Data Science, Statistics, TutorialsTags , , , , , , , 9 Comments on Be careful evaluating model predictions

Be careful evaluating model predictions

One thing I teach is: when evaluating the performance of regression models you should not use correlation as your score.

This is because correlation tells you if a re-scaling of your result is useful, but you want to know if the result in your hand is in fact useful. For example: the Mars Climate Orbiter software issued thrust commands in pound-seconds units to an engine expecting the commands to be in newton-seconds units. The two quantities are related by a constant ratio of 1.4881639, and therefore anything measured in pound-seconds units will have a correlation of 1.0 with the same measurement in newton-seconds units. However, one is not the other and the difference is why the Mars Climate Orbiter “encountered Mars at a lower than anticipated altitude and disintegrated due to atmospheric stresses.”

The need for a convenient direct F-test without accidentally triggering the implicit re-scaling that is associated with calculating a correlation is one of the reasons we supply the sigr R library. However, even then things can become confusing.


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Please read on for a nasty little example. Continue reading Be careful evaluating model predictions

Posted on Categories Mathematics, StatisticsTags , , , , , , 2 Comments on A budget of classifier evaluation measures

A budget of classifier evaluation measures

Beginning analysts and data scientists often ask: “how does one remember and master the seemingly endless number of classifier metrics?”

My concrete advice is:

  • Read Nina Zumel’s excellent series on scoring classifiers.
  • Keep notes.
  • Settle on one or two metrics as you move project to project. We prefer “AUC” early in a project (when you want a flexible score) and “deviance” late in a project (when you want a strict score).
  • When working on practical problems work with your business partners to find out which of precision/recall, or sensitivity/specificity most match their business needs. If you have time show them and explain the ROC plot and invite them to price and pick points along the ROC curve that most fit their business goals. Finance partners will rapidly recognize the ROC curve as “the efficient frontier” of classifier performance and be very comfortable working with this summary.

That being said it always seems like there is a bit of gamesmanship in that somebody always brings up yet another score, often apparently in the hope you may not have heard of it. Some choice of measure is signaling your pedigree (precision/recall implies a data mining background, sensitivity/specificity a medical science background) and hoping to befuddle others.


Mathmanship

Stanley Wyatt illustration from “Mathmanship” Nicholas Vanserg, 1958, collected in A Stress Analysis of a Strapless Evening Gown, Robert A. Baker, Prentice-Hall, 1963

The rest of this note is some help in dealing with this menagerie of common competing classifier evaluation scores.

Continue reading A budget of classifier evaluation measures