CompStats.measurements

CI(samples: ndarray, alpha=0.05)

Compute the Confidence Interval of a statistic using bootstrap. :param samples: Bootstrap samples :type samples: np.ndarray :param alpha: \([\frac{\alpha}{2}, 1 - \frac{\alpha}{2}]\). :type alpha: float

>>> from CompStats import StatisticSamples, CI
>>> from sklearn.metrics import accuracy_score
>>> import numpy as np    
>>> labels = np.r_[[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]]
>>> pred   = np.r_[[0, 0, 1, 0, 0, 1, 1, 1, 0, 1]]
>>> bootstrap = StatisticSamples(statistic=accuracy_score)
>>> samples = bootstrap(labels, pred)
>>> CI(samples)
(0.6, 1.0)

SE(samples: ndarray)

Compute the Standard Error of a statistic using bootstrap.

>>> from CompStats import StatisticSamples, SE
>>> from sklearn.metrics import accuracy_score
>>> import numpy as np    
>>> labels = np.r_[[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]]
>>> pred   = np.r_[[0, 0, 1, 0, 0, 1, 1, 1, 0, 1]]
>>> bootstrap = StatisticSamples(statistic=accuracy_score)
>>> samples = bootstrap(labels, pred)
>>> SE(samples)

difference_p_value(samples: ndarray, BiB: bool = True)

Compute the difference p-value