"Effective testing for machine learning systems requires both a traditional software testing suite (for model development infrastructure) and a model testing suite (for trained models)."
Read it!In a data science project, certain values such as file names, train-test split ratios, and hyperparameters often undergo frequent changes. By using configuration files instead of hard-coding these values, you can achieve better maintainability and flexibility.
Read it!A step-by-step implementation to gain a better understanding of how a decision tree works.
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