coming outt with some great stuff. I embedded this on my facebook, and mmy followers adored it.

Keep uup the good work 🙂 ]]>

Greeting and Regards

At first, thanks for learning and explain.

In your data set, you have some samples that each sample contains a number of attributes. for example, we have 100 samples that each sample contain 30 attributes.

My question is whether can we use this algorithm for a data set that has 100 samples with 30 attributes, Each feature has three parts? ,

i,e: we have a population of samples, that each sample contain 56 feature and each feature contains 3 parts. ]]>

Try this:

rs = ShuffleSplit(n_splits=10, test_size=0.3, random_state=42)

for train_idx, test_idx in rs.split(X):