split training data and testing data

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abdulaziz marie
abdulaziz marie 2018-1-18
评论： Abhijit Bhattacharjee 2023-3-4
Hello i have a 54000 x 10 matrix i want to split it 70% training and 30% testing whats the easiest way to do that ?
  1 个评论
Delvan Mjomba
Delvan Mjomba 2019-6-6
Use the Randperm command to ensure random splitting. Its very easy.
for example:
if you have 150 items to split for training and testing proceed as below:
Indices=randperm(150);
Trainingset=<data file name>(indices(1:105),:);
Testingset=<data file name>(indices(106:end),:);

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采纳的回答

Akira Agata
Akira Agata 2018-1-18
编辑：the cyclist 2022-8-16
I would recommend using cvpartition, like:
% Sample data (54000 x 10)
data = rand(54000,10);
% Cross varidation (train: 70%, test: 30%)
cv = cvpartition(size(data,1),'HoldOut',0.3);
idx = cv.test;
% Separate to training and test data
dataTrain = data(~idx,:);
dataTest = data(idx,:);
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Rishikesh Shetty
Rishikesh Shetty 2023-1-9
Hi Akira,
Thank you for this straight forward approach.
After following these steps, I was able to predict my model accuracy as expected.
My next question is - how do I split my data for all possible combinations?
For example, I have a 13*2 array that will split into 70/30 as 9*2 (training) and 4*2 (testing). I would like to repeat this split for all possible combinations(13C9) and then obtain an average of the model prediction accuracy.
Any advise is deeply appreciated.
Abhijit Bhattacharjee
Abhijit Bhattacharjee 2023-3-4
Rishikesh,
The CVPARTITION function randomizes the selection of the training and test datasets, so to get a new random combination just run it again. I am not sure it is advisable to try all combinatorial possibilities, as it is questionable whether that will return a much better model than you could get with considerably less effort. Just retrain with a new random partitioning a few times (say 10 times). This would be 10-fold cross-validation (or also called k-fold cross-validation for the case of k different random partitions).
Best,
Abhijit

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更多回答（4 个）

Gilbert Temgoua
Gilbert Temgoua 2022-4-19
编辑：Gilbert Temgoua 2022-4-20
I find dividerand very straightforward, see below:
% randomly select indexes to split data into 70%
% training set, 0% validation set and 30% test set.
[train_idx, ~, test_idx] = dividerand(54000, 0.7, 0,
0.3);
% slice training data with train indexes
%(take training indexes in all 10 features)
x_train = x(train_idx, :);
% select test data
x_test = x(test_idx, :);
  1 个评论
uma
uma 2022-4-28
how to split the data into trainx trainy testx testy format but both trainx trainy should have first dimension same also for testx testy should have first dimension same.Example i have a dataset 1000*9 . trainx should contain 1000*9, trainy should contain 1000*1, testx should contain 473*9 and texty should contain473*1.

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Vrushal Shah
Vrushal Shah 2019-3-14
If we want to Split the data set in Training and Testing Phase what is the best option to do that ?
Jere Thayo
Jere Thayo 2022-10-28
what if both training and testing are already in files, i.e X_train.mat, y_train.mat, x_test.mat and y_test.mat
Syed Iftikhar
Syed Iftikhar 2023-1-1
I have input variable name 's' in which i have data only in columns. The size is 1000000. I want to split that for 20% test. So i can save that data in some other variable. because i will gonna use that test data in some python script. Any Idea how to do this?

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