Hi guys, I am new with machine learning. I use classification learner and then export as model. My training dataset consist of 53 columns from x1 to x53 and 53 responses. Each column have 386 cells of numerical datas. From the confusion matrix I see that the algorithms seperate correctly the predictors x1...x53 into my two classes. The problem starts when I am trying to test the algorithm, I export the model via classification learner and import the testing file and then run it. The testin dataset consist 53 columns with 386 rows each, above each column I write x1..x53. The problem is the algorithm return 386 responses but it must return only 53. I think the algorithm check/predict rows instead of columns.
Any idea how to fix this?
A small piece of my dataset is shown below, it continues like this for 53 columns and 386 rows.