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.

gamma-facet

Human-explainable AI.

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.

greybox

Regression model building and forecasting in R

hyperactive

A unified interface for optimization algorithms and experiments

sklearn-genetic-opt

Hyperparameter optimization and feature selection for scikit-learn using evolutionary algorithms. A modern alternative to GridSearchCV and RandomizedSearchCV.

xrouter-llm

A prompt-aware LLM router that predicts which models can complete each request, then selects the cheapest capable one: 53.2% lower cost and +1.9 pts completion on our tested dataset.

tscv

Time Series Cross-Validation -- an extension for scikit-learn

petab-select

Model selection for PEtab

bayesloop

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

bxa

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

model-confidence-set

Model Confidence Set (MCS) implementation in Python

cascade-ml

ML-Engineering library

modelcreator

Simple python package for creating predictive models

kulprit

Kullback-Leibler projections for Bayesian model selection in Python

dissectml

The missing middle layer between EDA and AutoML — unified pipeline from deep data understanding to model comparison in 3 function calls. Deep EDA, 36-model battle arena, statistical significance testing, target leakage detection, and publication-ready HTML reports.

tpot2

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

bayes-compass

COMPASS: Comparison Of Models using Probabilistic Assessment in Simulation-based Settings

lazygrid

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