Package statkit
Statistics for machine learning.
Brings traditional (frequentistic) statistical concepts to your sci-kit learn models.
Examples
- Hypothesis testing of model scores with p-values (see, e.g.,
unpaired_permutation_test()), - Estimate 95% confidence intervals around test scores (see, e.g.,
bootstrap_score()). - Decision curve analysis to compare models in terms of consequences of actions
(see, e.g.,
NetBenefitDisplay). - Downsample a dataset while matching/stratifying on continuous/discrete variables
to balance the groups (see, e.g.,
balanced_downsample()). - Univariate feature selection with multiple hypothesis testing correction (see,
e.g.,
StatisticalTestFilter),
Installation
You can install statkit via pip from PyPI:
pip3 install statkit
Sub-modules
statkit.asserts-
Assert functions based on, and for, statistical tests.
statkit.dataset-
Various methods for partitioning the dataset, such as downsampling and splitting.
statkit.decision-
Evaluate models using decision curve analysis.
statkit.feature_selection-
Select features using statistical hypothesis testing.
statkit.metrics-
Classification metrics not part of sci-kit learn.
statkit.non_parametric-
Confidence intervals and p-values of a model's (test) score …
statkit.power-
Estimate population size needed to reject null hypothesis for a given metric.
statkit.typesstatkit.views