Package statkit

Statistics for machine learning.

Brings traditional (frequentistic) statistical concepts to your sci-kit learn models.

Examples

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.decision

Evaluate models using decision curve analysis.

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