Large data set for testing kernel logistic regression Does anyone know of a large data set (upwards of $10^7$ rows, but I'll take $10^5$ as well) that would be appropriate for testing kernel logistic regression?  Continuous variables, 2 to 50 independent variables?  Would prefer business-like data as opposed to scientific (weather, chemical, bio ...).
 A: Have a look at some of the data sets on the Elements of Statistical Learning textbook website.  They might be on the small side (most have between $10^2$ to $10^5$ observations, but it's a faily varied selection of datasets.
Specifically, you might want to look at:


*

*Marketing (may be a good business dataset)

A total of N=9409 questionnaires containg 502 questions were 
  filled out by shopping mall customers in the San Francisco Bay area. 
The dataset income.data is an extract from this survey. It consists of 
  14 demographic attributes. The dataset is a good mixture of categorical 
  and continuos variables with a lot of missing data. This is characteristic 
  for data mining applications. 
The goal is to predict the Anual Income of Household from the other 13
  demographics  attributes.


*Zip Code OCR (sorry, can't post more than two hyperlinks)

Normalized handwritten digits, automatically
  scanned from envelopes by the U.S. Postal Service. The original
  scanned digits are binary and of different sizes and orientations; the
  images  here have been deslanted and size normalized, resulting
  in 16 x 16 grayscale images (Le Cun et al., 1990).
The data are in two gzipped files, and each line consists of the digit
  id (0-9) followed by the 256 grayscale values.
There are 7291 training observations and 2007 test observations,
  distributed as follows:
         0    1   2   3   4   5   6   7   8   9 Total
Train 1194 1005 731 658 652 556 664 645 542 644 7291
Test   359  264 198 166 200 160 170 147 166 177 2007


