Blog Archives

In Search Of…

December 13, 2015
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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 y...

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In Search Of…

December 13, 2015
By
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 y...

Read more »

C5.0 Class Probability Shrinkage

September 14, 2015
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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...

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C5.0 Class Probability Shrinkage

September 14, 2015
By
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...

Read more »

Feature Engineering versus Feature Extraction: Game On!

August 3, 2015
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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...

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Feature Engineering versus Feature Extraction: Game On!

August 3, 2015
By
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...

Read more »

New caret Version (6.0-52)

July 22, 2015
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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...

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New caret Version (6.0-52)

July 22, 2015
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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...

Read more »

Slides from recent talks

April 21, 2015
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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 t...

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A Talk and Course in NYC Next Week

February 13, 2015
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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 choice of methodology? Where should we focus our efforts? The location is...

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