Which step involves assessing your model's performance using relevant evaluation metrics like precision and recall?

The step that involves assessing your model's performance using relevant evaluation metrics such as precision and recall is the evaluation and validation phase. During this stage, you systematically analyze how well your predictive model performs on both training and validation datasets. Precision and recall are crucial metrics for understanding the model's effectiveness, especially in scenarios where class imbalance may exist.

Precision evaluates how many selected items are relevant, while recall measures how many relevant items are selected. This comprehensive assessment allows for adjustments and tuning of the model to enhance accuracy and reliability before deployment. It ensures that the model meets desired performance standards, which is essential for any application, particularly in critical fields like medical billing and coding where correct interpretations can significantly impact outcomes.

In contrast, model development focuses on building the model itself, data preprocessing deals with preparing and cleaning the data, and implementation refers to deploying the model in a real-world environment. Each of these phases is important, but the specific task of evaluating performance using metrics such as precision and recall occurs distinctly in the evaluation and validation stage.