Parameter stratify from method train test split scikit Learn

Question

I am trying to use train test split from package scikit Learn  but I am having trouble with parameter stratify  Hereafter is the code  from sklearn import cross validation  datasets   X   iris data    2  y   iris target  cross validation train test split X y stratify y   However  I keep getting the following problem  raise TypeError  quot Invalid parameters passed   s quot    str options   TypeError  Invalid parameters passed    stratify   array  0  0  0  0  0  0  0  0  0  0  0   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0   0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1   1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  2   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2   2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2     Does someone have an idea what is going on  Below is the function documentation         stratify   array-like or None  default is None  If not None  data is split in a stratified fashion  using this as the labels array  New in version 0 17  stratify splitting

User · Answer

This stratify parameter makes a split so that the proportion of values in the sample produced will be the same as the proportion of values provided to parameter stratify    For example  if variable y is a binary categorical variable with values 0 and 1 and there are 25  of zeros and 75  of ones  stratify y will make sure that your random split has 25  of 0 s and 75  of 1 s

User · Answer

Scikit-Learn is just telling you it doesn t recognise the argument  stratify   not that you re using it incorrectly  This is because the parameter was added in version 0 17 as indicated in the documentation you quoted   So you just need to update Scikit-Learn

User · Answer

For my future self who comes here via Google   train test split is now in model selection  hence   from sklearn model selection import train test split    given    features  xs   ground truth  ys  x train  x test  y train  y test   train test split xs  ys                                                      test size 0 33                                                      random state 0                                                      stratify ys    is the way to use it  Setting the random state is desirable for reproducibility

User · Answer

Try running this code  it  just works    from sklearn import cross validation  datasets   iris   datasets load iris    X   iris data    2  y   iris target  x train  x test  y train  y test   cross validation train test split X y train size  8  stratify y   y test  array  0  0  0  0  2  2  1  0  1  2  2  0  0  1  0  1  1  2  1  2  0  2  2         1  2  1  1  0  2  1

User · Answer

In this context  stratification means that the train test split method returns training and test subsets that have the same proportions of class labels as the input dataset

[split] Parameter "stratify" from method "train_test_split" (scikit Learn)

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