Ethik AI
Ethik AI

Module ethik.regression_explainer

Expand source code 
from .cache_explainer import CacheExplainer

__all__ = ["RegressionExplainer"]


class RegressionExplainer(CacheExplainer):
    pass

Classes

class RegressionExplainer (alpha=0.05, n_taus=41, n_samples=1, sample_frac=0.8, conf_level=0.05, max_iterations=15, tol=0.0001, n_jobs=1, memoize=False, verbose=True)

Explains the influence of features on model predictions and performance.

Parameters

alpha : float
A float between 0 and 0.5 which indicates by how close the CacheExplainer should look at extreme values of a distribution. The closer to zero, the more so extreme values will be accounted for. The default is 0.05 which means that all values beyond the 5th and 95th quantiles are ignored.
n_taus : int
The number of τ values to consider. The results will be more fine-grained the higher this value is. However the computation time increases linearly with n_taus. The default is 41 and corresponds to each τ being separated by it's neighbors by 0.05.
n_samples : int
The number of samples to use for the confidence interval. If 1, the default, no confidence interval is computed.
sample_frac : float
The proportion of lines in the dataset sampled to generate the samples for the confidence interval. If n_samples is 1, no confidence interval is computed and the whole dataset is used. Default is 0.8.
conf_level : float
A float between 0 and 0.5 which indicates the quantile used for the confidence interval. Default is 0.05, which means that the confidence interval contains the data between the 5th and 95th quantiles.
max_iterations : int
The maximum number of iterations used when applying the Newton step of the optimization procedure. Default is 5.
tol : float
The bottom threshold for the gradient of the optimization procedure. When reached, the procedure stops. Otherwise, a warning is raised about the fact that the optimization did not converge. Default is 1e-4.
n_jobs : int
The number of jobs to use for parallel computations. See joblib.Parallel(). Default is -1.
memoize : bool
Indicates whether or not memoization should be used or not. If True, then intermediate results will be stored in order to avoid recomputing results that can be reused by successively called methods. For example, if you call plot_influence followed by plot_influence_ranking and memoize is True, then the intermediate results required by plot_influence will be reused for plot_influence_ranking. Memoization is turned off by default because it can lead to unexpected behavior depending on your usage.
verbose : bool
Whether or not to show progress bars during computations. Default is True.
Expand source code 
class RegressionExplainer(CacheExplainer):
    pass

Ancestors

Inherited members