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# `sklearn.datasets`.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_samples int, default=12000

      The number of samples.

    random_state int, 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:
    X ndarray of shape (n_samples, 10)

      The input samples.

    y ndarray 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`

Discrete versus Real AdaBoost

Discrete versus Real AdaBoost

Early stopping of Gradient Boosting

Early stopping of Gradient Boosting

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