Model Selection Python Packages

yellowbrick

Visual analysis and diagnostic tools to facilitate machine learning model selection.

dynesty

Dynamic Nested Sampling package for computing Bayesian posteriors and evidences

sklearndf

DataFrame support for scikit-learn.

tpot

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

evalml

EvalML is an AutoML library written in python.

ultranest

Fit and compare complex models reliably and rapidly. Advanced nested sampling.

shap-hypetune

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

gamma-facet

Human-explainable AI.

hyperactive

A unified interface for optimization algorithms and experiments

sklearn-genetic-opt

ML hyperparameters tuning and features selection, using evolutionary algorithms.

tscv

Time series cross-validation

bxa

Bayesian X-ray analysis (nested sampling for Xspec and Sherpa)

petab-select

Model selection for PEtab

cascade-ml

Lightweight and modular MLOps library targeted at small teams or individuals

lazygrid

Automatic, efficient and flexible implementation of complex machine learning pipeline generation and cross-validation.

tpot2

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

greybox

Toolbox for model building and forecasting

bayesloop

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

kulprit

Kullback-Leibler projections for Bayesian model selection in Python

insurance-cv

Temporal and distributional cross-validation, and feature screening, for insurance pricing models

modelcreator

Simple python package for creating predictive models

estyp

Extended Statistical Toolkit Yet Practical

model-confidence-set

Model Confidence Set (MCS) implementation in Python

easymlselector

A model selection process for Machine Learning tasks on subset of training sample