nested-cross-validation

Ensemble Integration: a customizable pipeline for generating multi-modal, heterogeneous ensembles

Nested Cross-Validation for Bayesian Optimized Gradient Boosting

Experimenting with various implementations and methods of nested cross-validation in R and Python

Nested Cross-Validation for Bayesian Optimized Linear Regularization

Python package customizing nested cross validation for tabular data.

Using scikit-learn RandomizedSearchCV and cross_val_score for ML Nested Cross Validation

A Knn algorithm used for train a model and prediction

Implementation of (Kernel) Ridge Regression predictors from scratch on Kaggle's Spotify Tracks Dataset.

Internally validated 30-day mortality prediction after acute myocardial infarction using ridge logistic regression, repeated nested cross-validation, calibration, bootstrap uncertainty and decision-curve analysis.

End-to-end MLOps pipeline predicting corporate bankruptcy from 95 financial ratios. Features nested CV, SHAP explainability, MLflow tracking, and Dockerized FastAPI deployment.

Python implementation of a nested cross-validation pipeline compatible with scikit-learn API.

❤️ 🩸 Blood test classifier for infected COVID-19 patients using xgb, catboost, rf and lr

nestkit: a nested cross-validation toolkit for scikit-learn

Predicting UK residential retrofit potential from EPC open data using Random Forest, XGBoost, Logistic Regression and SVM under nested cross-validation

🚀 Optimize TensorFlow models with the Nested Learning Optimizer for improved performance, faster convergence, and enhanced training stability.

Reproducible metabolomics classification research: subject-separated nested CV, model comparisons and an interactive synthetic-data showcase.

Reproducible multiclass leaf-classification benchmark with six ML models and bilingual portfolio reports

End-to-end resting-state EEG pipeline classifying younger (20-34) vs. older (52-70) adults from 405 people in OpenNeuro ds005385: MNE-Python preprocessing, Welch spectral features and nested cross-validated gradient boosting (ROC AUC 0.90, permutation p < 0.01).

Nested cross-validation study with Pearson correlation feature selection for breast cancer classification, reporting unbiased generalization estimates.