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Can Autosklearn handle Multi-Class/Multi-Label Classification and which classifiers will it use?
I have been trying to use AutoSklearn with Multi-class classification
so my labels are like this
0 1 2 3 4 ... 200
1 0 1 1 1 ... 1
0 1 0 0 1 ... 0
1 0 0 1 0 ... 0
1 1 0 1 0 ... 1
0 1 1 0 1 ... 0
1 1 1 0 0 ... 1
1 0 1 0 1 ... 0
I used this code
`
y = y[:, (65,67,54,133,122,63,102
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Describe the bug
Code could be more conform to pep8 and so forth.
Expected behavior
Less code st
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Description
Per https://discuss.ray.io/t/how-do-i-sample-from-a-ray-datasets/5308, we should add a
random_sample(N)
API that returns N records from a Dataset. This can be implemented via amap_batches()
followed by a take().cc @simon-mo @clarkzinzow
Use case
Random sample is useful for a variety of scenarios, including creating training batches, and downsampling the dataset for