curios
resources
presentations
lunch break
Distractions and amusements, with a sandwich and coffee.
fun + amusement
Dummer — Like Nothing Else
The Hummer font is a slightly modified Antique Olive Nord. The Like Nothing Else tag line is Trade Gothic. Both have character widths increased to 110-120% and individually adjusted kerning. Get the Illustrator CS5 file for both logos.
Download high-resolution images.
This project might give you the impression that I don't like Hummers. You'd be right.
update
The Maurauder. Over 25,000 lb — five times what an H3 weighs. Enough said.
Dummer - Like Nothing Else
Hummers are a cultural equivalent of a toxic warning label and have the same effect on me as bug spray on mosquitoes.
I am not the first one to satirize this automotive aberration, so there's some hope.
GM's advertisement images require no modification for the satire, which makes it all that much better.
I could have just as well used the Lincoln Navigator or Cadillac Escalade, but they don't embody the superlative like the Hummer.
The Hummer brand proved itself to be aesthetically, rationally and economically unsustainable and collapsed after a failed attempt to sell it to China. There continues to be a robust market for used Hummers. Let the farce continue.
I'm hated
It delights me that this project produced my first hate mail.
Only a Canadian and a liberal professor, would set up a website as ludicrous as Dummer.com.
Extremely Dumb!
If you are going to make fun of a Hummer, what about a Dodge powerwagon that obtains less miles per gallon? Many other vehicles on the road with worse mileage. But, I guess your location, and your profession tells it all.
Have a great day in BC...
Doug
I want to meet Doug and give him a hug for adding another dimension to this project.
I'm loved
The images got picked up by the New York Times laughlines blog, which drew a couple of fan mails.
Excellent work. One of the best ad parodies I've seen.
Gil
I don't normally write people to tell them I think their web work is good/bad, but I had to write and just say I think these are fucking brilliant. Should probably look into getting them made into billboards.
Dave
But neither made me feel as good as Doug's email.
Dummer Images
upcoming
news + thoughts
Model Selection and Overfitting
With four parameters I can fit an elephant and with five I can make him wiggle his trunk. —John von Neumann.
By increasing the complexity of a model, it is easy to make it fit to data perfectly. Does this mean that the model is perfectly suitable? No.
When a model has a relatively large number of parameters, it is likely to be influenced by the noise in the data, which varies across observations, as much as any underlying trend, which remains the same. Such a model is overfitted—it matches training data well but does not generalize to new observations.
We discuss the use of training, validation and testing data sets and how they can be used, with methods such as cross-validation, to avoid overfitting.
Altman, N. & Krzywinski, M. (2016) Points of Significance: Model Selection and Overfitting. Nature Methods 13:703-704.
Background reading
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Classifier evaluation. Nature Methods 13:603-604.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Logistic regression. Nature Methods 13:541-542.
Classifier Evaluation
It is important to understand both what a classification metric expresses and what it hides.
We examine various metrics use to assess the performance of a classifier. We show that a single metric is insufficient to capture performance—for any metric, a variety of scenarios yield the same value.
We also discuss ROC and AUC curves and how their interpretation changes based on class balance.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Classifier evaluation. Nature Methods 13:603-604.
Background reading
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Logistic regression. Nature Methods 13:541-542.
Happy 2016 `\pi` Approximation, roughly speaking
Today is the day and it's hardly an approximation. In fact, `22/7` is 20% more accurate of a representation of `\pi` than `3.14`!
Time to celebrate, graphically. This year I do so with perfect packing of circles that embody the approximation.
By warping the circle by 8% along one axis, we can create a shape whose ratio of circumference to diameter, taken as twice the average radius, is 22/7.
If you prefer something more accurate, check out art from previous `\pi` days: 2013 `\pi` Day and 2014 `\pi` Day, 2015 `\pi` Day, and 2016 `\pi` Day.
Logistic Regression
Regression can be used on categorical responses to estimate probabilities and to classify.
The next column in our series on regression deals with how to classify categorical data.
We show how linear regression can be used for classification and demonstrate that it can be unreliable in the presence of outliers. Using a logistic regression, which fits a linear model to the log odds ratio, improves robustness.
Logistic regression is solved numerically and in most cases, the maximum-likelihood estimates are unique and optimal. However, when the classes are perfectly separable, the numerical approach fails because there is an infinite number of solutions.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Logistic regression. Nature Methods 13:541-542.
Background reading
Altman, N. & Krzywinski, M. (2016) Points of Significance: Regression diagnostics? Nature Methods 13:385-386.
Altman, N. & Krzywinski, M. (2015) Points of Significance: Multiple Linear Regression Nature Methods 12:1103-1104.
Altman, N. & Krzywinski, M. (2015) Points of significance: Simple Linear Regression Nature Methods 12:999-1000.