ValueError: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than

[davidsbatista]

ValueError: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than

[valkiii]

Try using x_train, x_test, y_train, y_test = train_test_split(data_x,data_y,test_size=0.33, random_state=42) . It should work

[SHi-ON]

This because of the nature of stratification. The stratify parameter set it to split data in a way to allocate test_size amount of data to each class. In this case, you don't have sufficient class labels of one of your classes to keep the data splitting ratio equal to test_size.

[WajdiBenSaad]

This because of the nature of stratification. The stratify parameter set it to split data in a way to allocate test_size amount of data to each class. In this case, you don't have sufficient class labels of one of your classes to keep the data splitting ratio equal to test_size.

I confirm the above explanation. I have encountered this situation when dealing with a class that has a very low count . You can either take a random sample (not stratified) or try different test_size values, to be able to have an adequate size that could hold all your various labels.

[fatemerhmi]

It might be because you have a multi-label dataset. Which in this case you can use this tutorial from sklearn.

[fingoldo]

at has a very low count . You can either take a random sample (not stratified) or try different test_size values, to be able to have an adequate size that could hold all your various labels.

I think sklearn should handle such situations somehow automatically. It's frustrating and not clear immediately that it can be solved by slight fine-tuning of test_size.

[osancus]

I am having the same problem as @vikramkone can any suggest how i can solve it?

[shishir13]

I too faced the same issue. I was trying to solve the spam text classification problem wherein mostly we have less number of spam messages. But on seeing the count of spam and ham messages, I found out that they were both equal in numbers, and without looking into the count I applied stratify = data['label'], I removed the stratify part and I issue was solved.

[jiviteshoo7]

How can we fix this? I think random_state would be any integer because it only take permutation seeds from it.

[Adesoji1]

It might be because you have a multi-label dataset. Which in this case you can use this tutorial from sklearn.

Nope, my fake labels are 1,114 while real data labels are 475, now i i know this is the reason for ValueError: The least populated class in y has only 1 member, which is too few. The minimum number of groups for any class cannot be less than 2. @WajdiBenSaad is 101% correct. i am doing a binary classification problem