Cross Validation Python Packages

iterative-stratification

scikit-learn cross validators for iterative stratification of multilabel data

sklearndf

DataFrame support for scikit-learn.

timebasedcv

Time based splits for cross validation

autoreject

Automated rejection and repair of bad trials/sensors in M/EEG

sklearn-genetic-opt

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

omega-lock

Catch overfitting before you ship it: a walk-forward / held-out gate for any optimizer (Optuna, sweeps, prompt search). The highest score isn't deployable until it survives data it never saw. CLI + Python.

purgedcv

scikit-learn-compatible time-series cross-validation: purging, embargo, combinatorial purged CV, and deflated Sharpe ratios

temporalcv

Temporal cross-validation for time series

ml4t-diagnostic

Signal diagnostics, statistical validation, and backtest evaluation for quantitative trading workflows.

tscv

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

t1-t2-protocol

MCP server for heterogeneous AI validation — T1 structured reasoning, T2 cross-model evaluation, and deterministic checksum. Python stdlib-only.

nlptoolkit-sampling

Data sampling library

panelsplit

A tool for performing cross-validation with panel data

pychemometrics

pyChemometrics - Objects for multivariate analysis of chemometric and metabonomic datasets

nlptoolkit-sampling-cy

Data sampling library

jano

Temporal partitioning and backtesting utilities for time-correlated datasets.

cv-reality-check

Why your CV is lying to you

tide-feature-selection

Feature selection for real ML pipelines: filters, wrappers, and metaheuristics with cross-validation, reproducibility, and bundled benchmarks.

torchhandle

TorchHandle makes your PyTorch development more efficient and make you use PyTorch more comfortable

neuropredict

easy and standardized predictive analysis for biomarkers, neuroimaging, and beyond

autofepg

Automatic Feature Engineering & Selection for Kaggle Playground Competitions

lazygrid

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

osprey

🦅Hyperparameter optimization for machine learning pipelines 🦅

timeseries-cv

Time-Series Cross-Validation Module