Complete Guide to Cross Validation

Dive into a comprehensive 30-minute video tutorial on cross validation, an essential skill for machine learning practitioners. Learn how to implement cross validation techniques using scikit-learn in Python, with practical examples and code demonstrations. Explore the importance of avoiding overfitting and accurately assessing model performance. Follow along as the instructor covers setup, dataset introduction, common pitfalls, holdout checks, train-test splits, and various cross validation methods. Gain hands-on experience applying cross validation techniques to real-world scenarios. Access the accompanying Jupyter notebook for further practice and experimentation. Perfect for aspiring data scientists and machine learning enthusiasts looking to enhance their model evaluation skills.

Intro
Setup
The Dataset
The wrong way
Holdout check and baseline
Train/Test Split
Cross Validation
Applying Cross Validation