# Recursive logistic regression merge

I need to make regression on big amount of data, each row have around 1000 features. Did will outcome will be same or better when i make 4 separate regressions of 250 features and after that i will make one regression that will have 4 features equal to underlying regression outputs?

I can't make one regression on all features because coefficient learning algorithm using to much memory.

General known solution:

$Y' = sigmoid(X*\beta)$

My take:

$X' =$\begin{bmatrix} Y_1' = sigmoid(X_{1-250}*\beta_1) \\\ Y_2' = sigmoid(X_{251-500}*\beta_2) \\ Y_3' = sigmoid(X_{501-750}*\beta_3) \\ Y_4' = sigmoid(X_{751-1000}*\beta_4)\end{bmatrix} $Y'' = sigmoid(X'*\beta)$

I'm asking about differences/relationships between $Y'$ and $Y''$.

Rows (observations) around 100 000 000, features 1000 ($Length[X]$)

Sorry for bad formatting in equations, I'm not a MathJax master.

• How many observations (rows) do you have? How many "cases" (Y = 1) do you have? – D L Dahly Dec 22 '13 at 16:37
• @D L Dahly: around 100 * 10^6 rows. Cases Y = 1, around 50%, in some data can be 50,5%, classes are not skewed. – Svisstack Dec 22 '13 at 16:41
• Clearly you can't list all 1000 features, but can you give us some idea of the context and why you have so many? – Peter Flom - Reinstate Monica Dec 22 '13 at 18:09
• Potentially relevant – Glen_b -Reinstate Monica Dec 22 '13 at 22:53
• It is very unlikely that such an approach will be as good as fitting all 1000 features using penalized maximum likelihood estimation with a quadratic ($L_2$) penalty. – Frank Harrell Dec 23 '13 at 13:28