Correlate an array of categorical features to binary outcome I have a data set that looks like this:

target,items
1,[i1,i3]
1,[i4,i5,i9]
0,[i1]
...


The variable target is 0-1 outcome. The feature "items" is a set of items (variable length). Each item is a categorical variable (one of: i1, i2, .., i_N). There's no order/relationship between the items. A business example would be "set of products in a cart, outcome whether the customer abandons cart".

The size of data is approx. 1,000,000 by 5,000 (I have ~1 million examples, and N is approximately 5,000)

I want to do the following analysis. I want to find the items that influence (or lead to) target = 1. I don't have extra features to add. What is the type of statistical analysis or machine learning modelling technique that I should use?
How large is N?
Can you reshape your data into something like:

target   i1  i2  i3  i4  i5 ... i9 ... iN
     1    1   0   1   0   0 ...  0 ...  0        
     1    0   0   0   1   1 ...  1 ...  0
     0    1   0   0   0   0 ...  0 ...  0


Once you have fit everything into a data frame, you can use any two-class supervised classification algorithm to build your model. There is no "best" model in general, but try a few to see which one works the best with your data.

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