What does a generalized linear model do? R supplies a modeling function called `glm()`

that fits generalized linear models (abbreviated as GLMs). A natural question is what does it do and what problem is it solving for you? We work some examples and place generalized linear models in context with other techniques. Continue reading What does a generalized linear model do?

# Category: Pragmatic Machine Learning

## Modeling Trick: Impact Coding of Categorical Variables with Many Levels

One of the shortcomings of regression (both linear and logistic) is that it doesn’t handle categorical variables with a very large number of possible values (for example, postal codes). You can get around this, of course, by going to another modeling technique, such as Naive Bayes; however, you lose some of the advantages of regression — namely, the model’s explicit estimates of variables’ explanatory value, and explicit insight into and control of variable to variable dependence.

Here we discuss one modeling trick that allows us to keep categorical variables with a large number of values, and at the same time retain much of logistic regression’s power.

Continue reading Modeling Trick: Impact Coding of Categorical Variables with Many Levels

## Modeling Trick: Masked Variables

A primary problem data scientists face again and again is: how to properly adapt or treat variables so they are best possible components of a regression. Some analysts at this point delegate control to a shape choosing system like neural nets. I feel such a choice gives up far too much statistical rigor, transparency and control without real benefit in exchange. There are other, better, ways to solve the reshaping problem. A good rigorous way to treat variables are to try to find stabilizing transforms, introduce splines (parametric or non-parametric) or use generalized additive models. A practical or pragmatic approach we advise to get some of the piecewise reshaping power of splines or generalized additive models is: a modeling trick we call “masked variables.” This article works a quick example using masked variables. Continue reading Modeling Trick: Masked Variables

## Selection in R

The design of the statistical programming language R sits in a slightly uncomfortable place between the functional programming and object oriented paradigms. The upside is you get a lot of the expressive power of both programming paradigms. A downside of this is: the not always useful variability of the language’s list and object extraction operators.

Towards the end of our write-up Survive R we recommended using explicit environments with `new.env(hash=TRUE,parent=emptyenv())`

, `assign()`

and `get()`

to simulate mutable string-keyed maps for storing results. This advice rose out of frustration with the apparent inconsistency with the user facing R list operators. In this article we bite the bullet and discuss the R list operators a bit more clearly. Continue reading Selection in R

## Pragmatic Machine Learning

We are very excited to announce a new Win-Vector LLC blog category tag: Pragmatic Machine Learning. We don’t normally announce blog tags, but we feel this idea identifies an important theme common to a number of our articles and to what we are trying to help others achieve as data scientists. Please look for more news and offerings on this topic going forward. This is the stuff all data scientists need to know.

## Congratulations to both Dr. Nina Zumel and EMC- great job

A big congratulations to Win-Vector LLC‘s Dr. Nina Zumel for authoring and teaching portions of EMC‘s new Data Science and Big Data Analytics training and certification program. A big congratulations to EMC, EMC Education Services and Greenplum for creating a great training course. Finally a huge thank you to EMC, EMC Education Services and Greenplum for inviting Win-Vector LLC to contribute to this great project.

Continue reading Congratulations to both Dr. Nina Zumel and EMC- great job

## Setting expectations in data science projects

How is it even possible to set expectations and launch data science projects?

Data science projects vary from “executive dashboards” through “automate what my analysts are already doing well” to “here is some data, we would like some magic.” That is you may be called to produce visualizations, analytics, data mining, statistics, machine learning, method research or method invention. Given the wide range of wants, diverse data sources, required levels of innovation and methods it often feels like you can not even set goals for data science projects.

Many of these projects either fail or become open ended (become unmanageable).

As an alternative we describe some of our methods for setting quantifiable goals and front-loading risk in data science projects. Continue reading Setting expectations in data science projects

## Modeling Trick: the Signed Pseudo Logarithm

Much of the data that the analyst uses exhibits extraordinary range. For example: incomes, company sizes, popularity of books and any “winner takes all process”; (see: Living in A Lognormal World). Tukey recommended the logarithm as an important “stabilizing transform” (a transform that brings data into a more usable form prior to generating exploratory statistics, analysis or modeling). One benefit of such transforms is: data that is normal (or Gaussian) meets more of the stated expectations of common modeling methods like least squares linear regression. So data from distributions like the lognormal is well served by a `log()`

transformation (that transforms the data closer to Gaussian) prior to analysis. However, not all data is appropriate for a log-transform (such as data with zero or negative values). We discuss a simple transform that we call a signed pseudo logarithm that is particularly appropriate to signed wide-range data (such as profit and loss). Continue reading Modeling Trick: the Signed Pseudo Logarithm

## My Favorite Graphs

The important criterion for a graph is not simply how fast we can see a result; rather it is whether through the use of the graph we can see something that would have been harder to see otherwise or that could not have been seen at all.

— William Cleveland, *The Elements of Graphing Data*, Chapter 2

In this article, I will discuss some graphs that I find extremely useful in my day-to-day work as a data scientist. While all of them are helpful (to me) for statistical visualization during the analysis process, not all of them will necessarily be useful for presentation of final results, especially to non-technical audiences.

I tend to follow Cleveland’s philosophy, quoted above; these graphs show me — and hopefully you — aspects of data and models that I might not otherwise see. Some of them, however, are non-standard, and tend to require explanation. My purpose here is to share with our readers some ideas for graphical analysis that are either useful to you directly, or will give you some ideas of your own.

## Correlation and R-Squared

What is R^{2}? In the context of predictive models (usually linear regression), where *y* is the true outcome, and *f* is the model’s prediction, the definition that I see most often is:

In words, R^{2} is a measure of how much of the variance in *y* is explained by the model, *f*.

Under “general conditions”, as Wikipedia says, R^{2} is also the square of the correlation (correlation written as a “p” or “rho”) between the actual and predicted outcomes:

I prefer the “squared correlation” definition, as it gets more directly at what is usually my primary concern: prediction. If R^{2} is close to one, then the model’s predictions mirror true outcome, tightly. If R^{2} is low, then either the model does not mirror true outcome, or it only mirrors it loosely: a “cloud” that — hopefully — is oriented in the right direction. Of course, looking at the graph always helps:

The question we will address here is : how do you get from R^{2} to correlation?