Publications by Max Kuhn
Simulated Annealing Feature Selection
As previously mentioned, caret has two new feature selection routines based on genetic algorithms (GA) and simulated annealing (SA). The help pages for the two new functions give a detailed account of the options, syntax etc. The package already has functions to conduct feature selection using simple filters as well as recursive feature elimina...
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A Talk and Course in NYC Next Week
I’ll be giving talk on Tuesday February 17 (7:00PM-9:00PM) that will be an overview of predictive modeling. It will not be highly technical and here is the current outline: “Predictive modeling” definition Some example applications A short overview and example How is this different from what statisticians already do? What can drive choic...
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Slides from recent talks
I’ve been buried in work lately but thought I’d share the slides from two recent talks. The first is from the Bay Area RUG. Since someone filmed the talks, I was waiting to post the slides. The video of my talk (“Greatest Hits R Mixtape”) isn’t availible yet, so here are the slides. The second talk is from last week’s Thirteenth Annu...
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New caret Version (6.0-52)
A new version of caret (6.0-52) is on CRAN. Here is the news file but the Cliff Notes are: sub-sampling for class imbalances is now integrated with train and is used inside of standard resampling. There are four methods available right now: up- and down-sampling, SMOTE, and ROSE. The help page has detailed information. Nine additional models w...
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New caret Version (6.0-52)
A new version of caret (6.0-52) is on CRAN. Here is the news file but the Cliff Notes are: sub-sampling for class imbalances is now integrated with train and is used inside of standard resampling. There are four methods available right now: up- and down-sampling, SMOTE, and ROSE. The help page has detailed information. Nine additional models w...
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Feature Engineering versus Feature Extraction: Game On!
“Feature engineering” is a fancy term for making sure that your predictors are encoded in the model in a manner that makes it as easy as possible for the model to achieve good performance. For example, if your have a date field as a predictor and there are larger differences in response for the weekends versus the weekdays, then encoding the ...
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Feature Engineering versus Feature Extraction: Game On!
“Feature engineering” is a fancy term for making sure that your predictors are encoded in the model in a manner that makes it as easy as possible for the model to achieve good performance. For example, if your have a date field as a predictor and there are larger differences in response for the weekends versus the weekdays, then encoding the ...
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C5.0 Class Probability Shrinkage
(The image above has nothing do to with this post. It does, however, show the prize that my daughter won during a recent vacation to Virginia and how I got it back home). I was recently asked to explain a potential disconnect in C5.0 between the class probabilities shown in the terminal nodes and the values generated by the prediction code. Her...
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C5.0 Class Probability Shrinkage
(The image above has nothing do to with this post. It does, however, show the prize that my daughter won during a recent vacation to Virginia and how I got it back home). I was recently asked to explain a potential disconnect in C5.0 between the class probabilities shown in the terminal nodes and the values generated by the prediction code. Her...
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In Search Of…
Rafael Ladeira asked on github: I was wondering why it doesn’t implement some others algorithms for search for optimal tuning parameters. What would be the caveats of using a genetic algorithm , for instance, instead of grid or random search? Do you think using some of those powerful optimization algorithms for tuning parameters is a good ide...
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