y-aware scaling in context

Posted on Categories Exciting Techniques, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , Leave a comment on y-aware scaling in context

Nina Zumel introduced y-aware scaling in her recent article Principal Components Regression, Pt. 2: Y-Aware Methods. I really encourage you to read the article and add the technique to your repertoire. The method combines well with other methods and can drive better predictive modeling results.

From feedback I am not sure everybody noticed that in addition to being easy and effective, the method is actually novel (we haven’t yet found an academic reference to it or seen it already in use after visiting numerous clients). Likely it has been applied before (as it is a simple method), but it is not currently considered a standard method (something we would like to change).

In this note I’ll discuss some of the context of y-aware scaling. Continue reading y-aware scaling in context

Another note on differential privacy

Posted on Categories OpinionTags , , , Leave a comment on Another note on differential privacy

I want to recommend an excellent article on the recent claimed use of differential privacy to actually preserve user privacy: “A Few Thoughts on Cryptographic Engineering” by Matthew Green.

After reading the article we have a few follow-up thoughts on the topic. Continue reading Another note on differential privacy

Why you should read Nina Zumel’s 3 part series on principal components analysis and regression

Posted on Categories Administrativia, Exciting Techniques, Expository Writing, Statistics, TutorialsTags , , , Leave a comment on Why you should read Nina Zumel’s 3 part series on principal components analysis and regression

Short form:

Win-Vector LLC’s Dr. Nina Zumel has a three part series on Principal Components Regression that we think is well worth your time.

  • Part 1: the proper preparation of data (including scaling) and use of principal components analysis (particularly for supervised learning or regression).
  • Part 2: the introduction of y-aware scaling to direct the principal components analysis to preserve variation correlated with the outcome we are trying to predict.
  • Part 3: how to pick the number of components to retain for analysis.

Continue reading Why you should read Nina Zumel’s 3 part series on principal components analysis and regression

Free e-book: Exploring Data Science

Posted on Categories Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, StatisticsTags , , , , 3 Comments on Free e-book: Exploring Data Science

We are pleased to announce a new free e-book from Manning Publications: Exploring Data Science. Exploring Data Science is a collection of five chapters hand picked by John Mount and Nina Zumel, introducing you to various areas in data science and explaining which methodologies work best for each.

ExploringDataScience Continue reading Free e-book: Exploring Data Science

Using geom_step

Posted on Categories TutorialsTags , , , , 3 Comments on Using geom_step

geom_step is an interesting geom supplied by the R package ggplot2. It is an appropriate rendering option for financial market data and we will show how and why to use it in this article.

Continue reading Using geom_step

A demonstration of vtreat data preparation

Posted on Categories Exciting Techniques, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , , 8 Comments on A demonstration of vtreat data preparation

This article is a demonstration the use of the R vtreat variable preparation package followed by caret controlled training.

In previous writings we have gone to great lengths to document, explain and motivate vtreat. That necessarily gets long and unnecessarily feels complicated.

In this example we are going to show what building a predictive model using vtreat best practices looks like assuming you were somehow already in the habit of using vtreat for your data preparation step. We are deliberately not going to explain any steps, but just show the small number of steps we advise routinely using. This is a simple schematic, but not a guide. Of course we do not advise use without understanding (and we work hard to teach the concepts in our writing), but want what small effort is required to add vtreat to your predictive modeling practice.

Continue reading A demonstration of vtreat data preparation

On ranger respect.unordered.factors

Posted on Categories Expository Writing, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , 9 Comments on On ranger respect.unordered.factors

It is often said that “R is its packages.”

One package of interest is ranger a fast parallel C++ implementation of random forest machine learning. Ranger is great package and at first glance appears to remove the “only 63 levels allowed for string/categorical variables” limit found in the Fortran randomForest package. Actually this appearance is due to the strange choice of default value respect.unordered.factors=FALSE in ranger::ranger() which we strongly advise overriding to respect.unordered.factors=TRUE in applications. Continue reading On ranger respect.unordered.factors

Installing WVPlots and “knitting R markdown”

Posted on Categories Administrativia, TutorialsTags Leave a comment on Installing WVPlots and “knitting R markdown”

Some readers have been having a bit of trouble using devtools to install WVPlots (announced here and used to produce some of the graphs shown here). I thought I would write a note with a few instructions to help.

These are things you should not have to do often, and things those of us already running R have stumbled through and forgotten about. These are also the kind of finicky system dependent non-repeatable interactive GUI steps you largely avoid once you have a scriptable system like fully R up and running. Continue reading Installing WVPlots and “knitting R markdown”

For a short time: Half Off Some Manning Data Science Books

Posted on Categories Administrativia, Pragmatic Data Science, StatisticsTags , Leave a comment on For a short time: Half Off Some Manning Data Science Books

Our publisher Manning Publications is celebrating the release of a new data science in Python title Introducing Data Science by offering it and other Manning titles at half off until Wednesday, May 18.

As part of the promotion you can also use the supplied discount code mlcielenlt for half off some R titles including R in Action, Second Edition and our own Practical Data Science with R. Combine these with our half off code (C3) for our R video course Introduction to Data Science and you can get a lot of top quality data science material at a deep discount.

Coming up: principal components analysis

Posted on Categories Administrativia, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , 2 Comments on Coming up: principal components analysis

Just a “heads-up.”

I’ve been editing a two-part three-part series Nina Zumel is writing on some of the pitfalls of improperly applied principal components analysis/regression and how to avoid them (we are using the plural spelling as used in following Everitt The Cambridge Dictionary of Statistics). The series is looking absolutely fantastic and I think it will really help people understand, properly use, and even teach the concepts.

The series includes fully worked graphical examples in R and is why we added the ScatterHistN plot to WVPlots (plot shown below, explained in the upcoming series).

s

Frankly the material would have worked great as an additional chapter for Practical Data Science with R (but instead everybody is going to get it for free).

Please watch here for the series.
The complete series is now up: