What are the best practices for testing in data science?
An example of an AI chatbot conversation about best practices in data science testing.
What are the best practices for testing in data science?
Some best practices include using cross-validation, maintaining a separate test dataset, and automating tests.
Can you explain why cross-validation is important?
Cross-validation helps ensure that your model generalizes well to unseen data by testing it on different subsets.
What tools can I use for automating tests in data science?
You can use tools like pytest, unittest, or specific libraries like Great Expectations for data validation.
How often should I test my models?
It's best to test your models regularly, especially after any significant changes to the data or model architecture.
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