I have a dataset for cancer and non-cancer patients and would like to prepare it for classification.

Each sample has 4 columns and 1298 rows. The total number of samples is 68. So my X_train shape is: (68, 1298, 4) and Y_train shape is (68).

Now, if I reshape the data to be in 2D array, how can I tell the model to separate these 68 samples?

My question is: how should I reshape the dataset and how should be the input shape format to the model?

  • $\begingroup$ Welcome to DataScienceSE. The basic method is to just concatenate everything in a sample, so you obtain 1298 * 4 columns for every sample. For a more specific answer you should probably give more detail about what the data represents. $\endgroup$
    – Erwan
    Feb 2 at 22:15

1 Answer 1


To make the data amenable to machine learning, each row should be an instance and each column should be a feature.

You state that you have 68 samples/instances, those are the rows. It is unclear from your description how to structure the 1298 x 4 dimensions. One brute force method is to take the Cartesian product which would result in 5,192 columns/features. The result is a matrix that is 68x5192 and ready for classification. The number of columns/features may be reduced through feature engineering and/or feature selection.


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