sklearn.datasets.make_hastie_10_2

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

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_samples : int, optional (default=12000)

The number of samples.

random_state : int, RandomState instance or None (default)

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

Returns:

X : array of shape [n_samples, 10]

The input samples.

y : array 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

Discrete versus Real AdaBoost

Early stopping of Gradient Boosting

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV