make_hastie_10_2

sklearn.datasets.make_hastie_10_2(n_samples=12000, *, random_state=None)

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.

See also

make_gaussian_quantiles

A generalization of this dataset approach.

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])
[-1.0, 1.0, -1.0, 1.0, -1.0]

Gallery examples

Gradient Boosting regularization

Gradient Boosting regularization

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV