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_samples : int, optional (default=12000) The number of samples. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
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

[R144]

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

Gradient Boosting regularization