bootstrap
bootstrap(x, func, num_rounds=1000, ci=0.95, ddof=1, seed=None)
Implements the ordinary nonparametric bootstrap
Parameters
-
x
: NumPy array, shape=(n_samples, [n_columns])An one or multidimensional array of data records
-
func
:A function which computes a statistic that is used to compute the bootstrap replicates (the statistic computed from the bootstrap samples). This function must return a scalar value. For example,
np.mean
ornp.median
would be an acceptable argument forfunc
ifx
is a 1-dimensional array or vector. -
num_rounds
: int (default=1000)The number of bootstrap samnples to draw where each bootstrap sample has the same number of records as the original dataset.
-
ci
: int (default=0.95)An integer in the range (0, 1) that represents the confidence level for computing the confidence interval. For example,
ci=0.95
(default) will compute the 95% confidence interval from the bootstrap replicates. -
ddof
: intThe delta degrees of freedom used when computing the standard error.
-
seed
: int or None (default=None)Random seed for generating bootstrap samples.
Returns
-
original, standard_error, (lower_ci, upper_ci)
: tupleReturns the statistic of the original sample (
original
), the standard error of the estimate, and the respective confidence interval bounds.
Examples
>>> from mlxtend.evaluate import bootstrap
>>> rng = np.random.RandomState(123)
>>> x = rng.normal(loc=5., size=100)
>>> original, std_err, ci_bounds = bootstrap(x,
... num_rounds=1000,
... func=np.mean,
... ci=0.95,
... seed=123)
>>> print('Mean: %.2f, SE: +/- %.2f, CI95: [%.2f, %.2f]' % (original,
... std_err,
... ci_bounds[0],
... ci_bounds[1]))
Mean: 5.03, SE: +/- 0.11, CI95: [4.80, 5.26]
>>>
For more usage examples, please see http://rasbt.github.io/mlxtend/user_guide/evaluate/bootstrap/