make_hastie_10_2 (original) (raw)

sklearn.datasets.make_hastie_10_2(n_samples=12000, *, random_state=None)[source]#

Generate data for binary classification used in Hastie et al. 2009, Example 10.2.

The ten features are standard independent Gaussian and the target y is defined by:

y[i] = 1 if np.sum(X[i] ** 2) > 9.34 else -1

Read more in the User Guide.

Parameters:

n_samplesint, default=12000

The number of samples.

random_stateint, RandomState instance or None, default=None

Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns:

Xndarray of shape (n_samples, 10)

The input samples.

yndarray of shape (n_samples,)

The output values.

References

[1]

T. Hastie, R. Tibshirani and J. Friedman, “Elements of Statistical Learning Ed. 2”, Springer, 2009.

Examples

from sklearn.datasets import make_hastie_10_2 X, y = make_hastie_10_2(n_samples=24000, random_state=42) X.shape (24000, 10) y.shape (24000,) list(y[:5]) [np.float64(-1.0), np.float64(1.0), np.float64(-1.0), np.float64(1.0), np.float64(-1.0)]

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