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`sklearn.datasets`.make_hastie_10_2¶

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

Generates 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¶

Discrete versus Real AdaBoost — Discrete versus Real AdaBoost¶

Early stopping of Gradient Boosting — Early stopping of Gradient Boosting¶

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV — Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV¶