reduce No of dimensions

ui3o

Dear all,

here's what keeps me busy as a newbie to DataMining: my example set contains ~70 k examples of process parameters from 5 machines and a quality label (0/1). each machine parameter has 3-5 variations (3-5 attributes per parameter and machine). Most - but not all - variations referring to the same machine parameter correlate strongly and I was thinking to eliminate "all but one" in such a case, in order to do logistic regression.

When playing arround with weights from correlation matrix, weight by correlation, select by weight, etc. I was only able to eliminate all attributes, which correlate with others, but I want to keep one.

My target is obviously, to identify those attributes, which are influencing the label most.

Maybe I am methodologically totally wrong here, so I appreciate any help.

Thx


ui3o

radone

Hello,
for your purpose the operator "Remove Correlated Attributes" could do the job. The possible pitfall can be memory consumption - few months ago I have used the operator "Select by Random" for number of attributes reduction.