Module 3: Cross-Validation and Grid Search
Module Overview
In this module, you will learn essential techniques for properly validating machine learning models and optimizing their hyperparameters. Cross-validation helps ensure that your model performance estimates are reliable, while grid search provides a systematic approach to finding the best hyperparameters for your models.
You'll learn how to implement these techniques using scikit-learn, and understand their importance in building models that generalize well to new, unseen data.
Learning Objectives
- Implement k-fold cross validation
- Use scikit-learn for hyperparameter optimization
Guided Project
Open JDS_SHR_223_guided_project_notes.ipynb in the GitHub repository below to follow along with the guided project:
Guided Project Video
Module Assignment
Complete the Module 3 assignment to practice cross-validation and hyperparameter optimization techniques you've learned.
Continue improving your Kaggle competition submission by implementing cross-validation and hyperparameter optimization.