See the scikit-learn cross validation documentation for a fuller discussion of cross validation. This document only describes the extensions made to support Dask arrays.
The simplest way to split one or more Dask arrays is with
In : import dask.array as da In : from dask_ml.datasets import make_regression In : from dask_ml.model_selection import train_test_split In : X, y = make_regression(n_samples=125, n_features=4, random_state=0, chunks=50) In : X Out: dask.array<normal, shape=(125, 4), dtype=float64, chunksize=(50, 4)>
The interface for splitting Dask arrays is the same as scikit-learn’s version.
In : X_train, X_test, y_train, y_test = train_test_split(X, y) In : X_train # A dask Array Out: dask.array<concatenate, shape=(112, 4), dtype=float64, chunksize=(45, 4)> In : X_train.compute()[:3]