When studying regression models, One of the first diagnostic plots most students learn is to plot residuals versus the model’s predictions (that is, with the predictions on the x-axis). Here’s a basic example. # build an “ideal” linear process. set.seed(34524) N = 100 x1 = runif(N) x2 = runif(N) noise […]

Estimated reading time: 9 minutes

This note is about attempting to remove the bias brought in by using sample standard deviation estimates to estimate an unknown true standard deviation of a population. We establish there is a bias, concentrate on why it is not important to remove it for reasonable sized samples, and (despite that) […]

Estimated reading time: 11 minutes

In statistical work in the age of big data we often get hung up on differences that are statistically significant (reliable enough to show up again and again in repeated measurements), but clinically insignificant (visible in aggregation, but too small to make any real difference to individuals). An example would […]

Estimated reading time: 2 minutes

I am pleased to announce that vtreat version 0.6.0 is now available to R users on CRAN. vtreat is an excellent way to prepare data for machine learning, statistical inference, and predictive analytic projects. If you are an R user we strongly suggest you incorporate vtreat into your projects.

Estimated reading time: 2 minutes

Data preparation and cleaning are some of the most important steps of predictive analytic and data science tasks. They are laborious, where most of the errors are made, your last line of defense against a wild data, and hold the biggest opportunities for outcome improvement. No matter how much time […]

Estimated reading time: 3 minutes

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 […]

Estimated reading time: 9 minutes

Nina Zumel and I have been doing a lot of writing on the (important) details of re-encoding high cardinality categorical variables for predictive modeling. These are variables that essentially take on string-values (also called levels or factors) and vary through many such levels. Typical examples include zip-codes, vendor IDs, and […]

Estimated reading time: 5 minutes

Nina Zumel recently announced upcoming speaking appearances. I want to promote the upcoming sessions at ODSC West 2016 (11:15am-1:00pm on Friday November 4th, or 3:00pm-4:30pm on Saturday November 5th) and invite executives, managers, and other data science consumers to attend. We assume most of the Win-Vector blog audience is made […]

Estimated reading time: 3 minutes

In our last article on the algebra of classifier measures we encouraged readers to work through Nina Zumel’s original “Statistics to English Translation” series. This series has become slightly harder to find as we have use the original category designation “statistics to English translation” for additional work. To make things […]

Estimated reading time: 1 minute

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 […]

Estimated reading time: 15 minutes