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📓 Interactive Jupyter Notebooks

Hands-on coding tutorials covering all 29 cross-validation methods for medical machine learning. Learn by implementing real examples with clinical datasets and best practices.

5 Comprehensive Notebooks Google Colab Ready Real Medical Datasets

🎯 Structured Learning Path

Our notebooks are organized in a progressive learning sequence, from fundamentals to advanced techniques. Each notebook builds on previous concepts while introducing new medical-specific validation challenges.

school

Beginner

Master the fundamentals of cross-validation with medical data

Notebook 1
construction

Intermediate

TrustCVValidator workflows and integration

Notebooks 2-4
psychology

Advanced

Comprehensive CV method comparison

Notebook 5
01 Beginner
Open In Colab

I.I.D. Methods Showcase

Complete demonstration of all 9 I.I.D. cross-validation methods with visualizations. Covers HoldOut, KFold, StratifiedKFold, RepeatedKFold, LOOCV, LPOCV, Bootstrap, MonteCarloCV, and NestedCV.

K-Fold CV Bootstrap .632 LOOCV/LPOCV Nested CV
schedule 60 minutes
dataset Breast Cancer
02 Intermediate
Open In Colab

Advanced Workflow with UniversalRunner

Learn to use the UniversalCVRunner for advanced cross-validation workflows. Integrate with different ML frameworks and customize validation pipelines.

UniversalCVRunner Custom Pipelines Framework Integration Advanced Workflows
schedule 45 minutes
dataset Medical Datasets
03 Intermediate
Open In Colab

TrustCVValidator Showcase

Comprehensive guide to using TrustCVValidator for medical ML validation. Includes leakage detection, balance checking, and confidence interval estimation.

TrustCVValidator Leakage Detection Confidence Intervals Medical Metrics
schedule 50 minutes
dataset Clinical Data
04 Intermediate
Open In Colab

TrustCVValidator - IID Methods Comparison

Compare I.I.D. cross-validation methods using TrustCVValidator. Benchmarking different CV strategies on the same medical dataset.

Method Comparison Benchmarking Performance Analysis Best Practices
schedule 55 minutes
dataset Breast Cancer
05 Advanced
Open In Colab

Comprehensive Cross-Validation Comparison

Compare trustcv with sklearn, XGBoost, LightGBM, CatBoost, PyCaret, H2O, Keras, and PyTorch. Shows how trustcv integrates with different ML ecosystems.

Multi-Framework XGBoost/LightGBM Keras/PyTorch H2O/PyCaret
schedule 70 minutes
dataset Breast Cancer
06 Advanced
Open In Colab

Deep Learning Showcase & Glossary

Advanced validation for deep learning models (TensorFlow, PyTorch, MONAI). Includes a comprehensive glossary of trustworthy CV terms.

Deep Learning MONAI/Imaging CV Glossary Advanced Validation
schedule 75 minutes
dataset Multi-Modal

🚀 Getting Started

cloud

Run in Google Colab

Click any "Open in Colab" button to run notebooks in the cloud. No installation required!

✅ Free GPU access
✅ Pre-installed libraries
✅ Real medical datasets
download

Local Installation

Clone the repository and run notebooks locally with Jupyter Lab or Jupyter Notebook.

git clone https://github.com/ki-smile/trustcv.git
cd trustcv
pip install -e .
jupyter lab notebooks/
route

Learning Path

Follow our structured sequence for optimal learning. Each notebook builds on previous concepts.

1. Start with fundamentals (1-3)
2. Master advanced techniques (4-7)
3. Apply comprehensive methods (8-10)