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Smaller test data set than training data set in machine learning

Quick guide into training highly imbalanced data setsPre-processing (center, scale, impute) among training sets (different forms) and the test set - what is a good approach?Does Dataset training and test size affect algorithm?GANs to augment training dataShould I prevent augmented data to leak to the test/cross validation setsWhy would 2 sets of similar training samples take significantly longer to train?Is it correct to use non-target values of test set to engineer new features for train set?Validation accuracy is always close to training accuracyPerformance of model in production varying greatly from train-test dataWhat could cause validation set to consistently perform better than training?

0

$begingroup$

I would like to train different machine learning algorithms (SVM, Random Forest, CNN etc.) for the same data set (e.g. MNIST) und then compare their accuracies.
The goal would be to find out from which training data size which method is preferable to the others.
To do this I continuously reduce the original training data set (of 60000 samples) and train the models on these reduced trainings data sets.

If I then determine the accuracy using the original MNIST-test dataset (10000 samples), of course I will get overfitting, e.g. with a training data set of 1000 samples I get a training accuracy of 95% and test accuracy of 75%.

The smaller the training data set, the lower the test accuracy, while the training accuracy remains at about the same level.

Would it make sense also to reduce the test data set to restore the original 1:6 ratio of the test set : training set?
Personally, I think that does not make sense.
Or have I thought incorrectly about that?

dataset cnn svm accuracy

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add a comment | 

0

$begingroup$

I would like to train different machine learning algorithms (SVM, Random Forest, CNN etc.) for the same data set (e.g. MNIST) und then compare their accuracies.
The goal would be to find out from which training data size which method is preferable to the others.
To do this I continuously reduce the original training data set (of 60000 samples) and train the models on these reduced trainings data sets.

If I then determine the accuracy using the original MNIST-test dataset (10000 samples), of course I will get overfitting, e.g. with a training data set of 1000 samples I get a training accuracy of 95% and test accuracy of 75%.

The smaller the training data set, the lower the test accuracy, while the training accuracy remains at about the same level.

Would it make sense also to reduce the test data set to restore the original 1:6 ratio of the test set : training set?
Personally, I think that does not make sense.
Or have I thought incorrectly about that?

dataset cnn svm accuracy

share

asked 6 mins ago

Code NowCode Now

1

$endgroup$

add a comment | 

$begingroup$

I would like to train different machine learning algorithms (SVM, Random Forest, CNN etc.) for the same data set (e.g. MNIST) und then compare their accuracies.
The goal would be to find out from which training data size which method is preferable to the others.
To do this I continuously reduce the original training data set (of 60000 samples) and train the models on these reduced trainings data sets.

If I then determine the accuracy using the original MNIST-test dataset (10000 samples), of course I will get overfitting, e.g. with a training data set of 1000 samples I get a training accuracy of 95% and test accuracy of 75%.

The smaller the training data set, the lower the test accuracy, while the training accuracy remains at about the same level.

Would it make sense also to reduce the test data set to restore the original 1:6 ratio of the test set : training set?
Personally, I think that does not make sense.
Or have I thought incorrectly about that?

dataset cnn svm accuracy

share

asked 6 mins ago

Code NowCode Now

1

$endgroup$

share

asked 6 mins ago

Code NowCode Now

1

share

asked 6 mins ago

Code NowCode Now

1

asked 6 mins ago

Code NowCode Now

1

asked 6 mins ago

Code NowCode Now

1