The release date for Zumel, Mount “Practical Data Science with R” is getting close. I thought I would share a few statistics about what goes into this kind of book. “Practical Data Science with R” started formal work in October of 2012. We had always felt the Win-Vector blog represented practice and research for such […] Related posts: On writing a technical book Book Review: Ensemble Methods in Data Mining…
Some researchers (in both science and marketing) abuse a slavish view of p-values to try and falsely claim credibility. The incantation is: “we achieved p = x (with x ≤ 0.05) so you should trust our work.” This might be true if the published result had been performed as a single project (and not as […] Related posts: Bayesian and Frequentist Approaches: Ask the Right Question Worry about correctness and…
Please forward and share this discount offer for our upcoming book. Manning Deal of the Day February 22: Half off Practical Data Science with R. Use code dotd022214au at www.manning.com/zumel/. Related posts: Data Science, Machine Learning, and Statis...
The Facebook data science blog shared some fun data explorations this Valentine’s Day in Carlos Greg Diuk’s “The Formation of Love”. They are rightly receiving positive interest in and positive reviews of their work (for example Robinson Meyer’s Atlantic article). The finding is also a great opportunity to discuss the gap between cool data mining […] Related posts: A Demonstration of Data Mining Generalized linear models for predicting rates Data…
As a data scientist I have seen variations of principal component analysis and factor analysis so often blindly misapplied and abused that I have come to think of the technique as unprincipled component analysis. PCA is a good technique often used to reduce sensitivity to overfitting. But this stated design intent leads many to (falsely) […] Related posts: Bad Bayes: an example of why you need hold-out testing Don’t use…
We demonstrate a dataset that causes many good machine learning algorithms to horribly overfit. The example is designed to imitate a common situation found in predictive analytic natural language processing. In this type of application you are often building a model using many rare text features. The rare text features are often nearly unique k-grams […] Related posts: Don’t use correlation to track prediction performance Generalized linear models for predicting…
Nassim Nicholas Taleb recently wrote an article advocating the abandonment of the use of standard deviation and advocating the use of mean absolute deviation. Mean absolute deviation is indeed an interesting and useful measure- but there is a reason that standard deviation is important even if you do not like it: it prefers models that […] Related posts: Don’t use correlation to track prediction performance What does a generalized linear…
I often need to build a predictive model that estimates rates. The example of our age is: ad click through rates (how often a viewer clicks on an ad estimated as a function of the features of the ad and the viewer). Another timely example is estimating default rates of mortgages or credit cards. You […] Related posts: What does a generalized linear model do? The equivalence of logistic regression…
We have written a bit on sample size for common events, we have written about rare events, and we have written about frequentist significance testing. We would like to specialize our sample size analysis to rare events (which allows us to derive a somewhat tighter estimate). In web marketing and a lot of other applications […] Related posts: Estimating rates from a single occurrence of a rare event A bit…
Please share: Manning Deal of the Day November 19: Half off Practical Data Science with R. Use code dotd1119au at www.manning.com/zumel/. Related posts: Data Science, Machine Learning, and Statistics: what is in a name? Data science project planning S...
