`sklearn.datasets`.make_hastie_10_2#

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.

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 using `sklearn.datasets.make_hastie_10_2`#

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