Package statkit
Statistics for machine learning.
Brings traditional (frequentistic) statistical concepts to your sci-kit learn models.
Examples
- Univariate feature selection with multiple hypothesis testing correction (see, e.g.,
StatisticalTestFilter
), - 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()
).
Expand source code
r"""Statistics for machine learning.
Brings traditional (frequentistic) statistical concepts to your sci-kit learn models.
Examples:
- Univariate feature selection with multiple hypothesis testing correction (see, e.g.,
`statkit.feature_selection.StatisticalTestFilter`),
- Hypothesis testing of model scores with \(p\)-values (see, e.g.,
`statkit.non_parametric.unpaired_permutation_test`),
- Estimate 95 % confidence intervals around test scores (see, e.g.,
`statkit.non_parametric.bootstrap_score`).
"""
Sub-modules
statkit.distributions
-
Extension of pomegranate distributions to support pseudo counts and value inflation.
statkit.feature_selection
-
Select features using statistical hypothesis testing.
statkit.metrics
-
Classification metrics not part of sci-kit learn.
statkit.naive_bayes
-
Naive Bayes classifier with support for feature specific distributions …
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.types
statkit.views