Develop Test Suites for Machine Learning Models and Data with Deepchecks

Develop test suites for machine learning models and data using Deepchecks in this hands-on lab. Explore the Python library's extensive test suites, learn to compose checks with customizable conditions, and generate HTML reports for easy result interpretation. Master data validation tests, distribution analysis, and model validation techniques. Create custom tests tailored to specific needs, and integrate Deepchecks into ML pipelines for comprehensive model and data quality assurance. Apply these skills to Random Forest and Gradient Boosting Classifier models, gaining practical experience in enhancing the reliability and performance of machine learning projects.

- Video start
- Lab Intro
- When to use Deepchecks
- Getting started
- Test Suite Functions
- Integration in ML Pipeline
- Data Validation Tests
- Individual test setup
- Test result HTML
- Data Distribution Tests
- Data Distribution Tests 2
- Writing custom tests
- Model analysis and validation tests
- Random Forest classifier model tests
- Gradient Boosting Classifier model tests
- Code push to GitHub
- Lab Recap
- Credits