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I have a dataset with 26 features and 500031000 rows. It is the dataset of 38 subjects. It is for a biometric system. So I want to be able to identify subjects.

In order to have a testing set, I know I have to remove some values.

So what is it better to do and why?

  1. (a) keep 30 subjects as training set and remove 58 subjects as testing set

  2. (b) keep the 38 subjects, but remove some rows of each one. So at the end I will end up with a training set: 300024800 rows of 38 subjects AND a testing set: 20006200 rows of 38 subjects

I have a dataset with 26 features and 5000 rows. It is the dataset of 38 subjects.

In order to have a testing set, I know I have to remove some values.

So what is it better to do and why?

  1. (a) keep 30 subjects as training set and remove 5 subjects as testing set

  2. (b) keep the 38 subjects, but remove some rows of each one. So at the end I will end up with a training set: 3000 rows of 38 subjects AND a testing set: 2000 rows of 38 subjects

I have a dataset with 26 features and 31000 rows. It is the dataset of 38 subjects. It is for a biometric system. So I want to be able to identify subjects.

In order to have a testing set, I know I have to remove some values.

So what is it better to do and why?

  1. (a) keep 30 subjects as training set and remove 8 subjects as testing set

  2. (b) keep the 38 subjects, but remove some rows of each one. So at the end I will end up with a training set: 24800 rows of 38 subjects AND a testing set: 6200 rows of 38 subjects

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What is the more appropriate way to create a hold-out set: to remove some subjects or to remove some observations from each subject?

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Which of these 2 techniques What is mostmore appropriate to create a hold-out set: to remove some subjects or to remove some observations from each subject?

I have a dataset with 26 features and 5000 rows. It is the dataset of 38 subjects.

In order to have a testing set, I know I have to remove some values.

So what is it better to do and why?

a) keep 30 subjects as training set and remove 5 subjects as testing set

b) keep the 38 subjects, but remove some rows of each one. So at the end I will end up with a training set: 3000 rows of 38 subjects AND a testing set: 2000 rows of 38 subjects

  1. (a) keep 30 subjects as training set and remove 5 subjects as testing set

  2. (b) keep the 38 subjects, but remove some rows of each one. So at the end I will end up with a training set: 3000 rows of 38 subjects AND a testing set: 2000 rows of 38 subjects

Which of these 2 techniques is most appropriate to create a hold-out set?

I have a dataset with 26 features and 5000 rows. It is the dataset of 38 subjects.

In order to have a testing set, I know I have to remove some values.

So what is it better to do and why?

a) keep 30 subjects as training set and remove 5 subjects as testing set

b) keep the 38 subjects, but remove some rows of each one. So at the end I will end up with a training set: 3000 rows of 38 subjects AND a testing set: 2000 rows of 38 subjects

What is more appropriate to create a hold-out set: to remove some subjects or to remove some observations from each subject?

I have a dataset with 26 features and 5000 rows. It is the dataset of 38 subjects.

In order to have a testing set, I know I have to remove some values.

So what is it better to do and why?

  1. (a) keep 30 subjects as training set and remove 5 subjects as testing set

  2. (b) keep the 38 subjects, but remove some rows of each one. So at the end I will end up with a training set: 3000 rows of 38 subjects AND a testing set: 2000 rows of 38 subjects

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