Evaluation

In this section, we explore model evaluation, a phase that’s often underestimated but critically important. This is our chance to show we’re not just optimizing metrics—we’re aligning our work with real-world impact.

• Mediocre evaluation involves simply calling .score() or reporting accuracy, with little explanation or context—a major red flag in interviews.
• Stellar candidates choose metrics that fit the problem, such as F1 score or AUC-ROC for imbalanced classification, or MAE over RMSE for certain regression tasks.
• We demonstrate awareness of trade-offs, like how boosting recall might reduce precision, and explain the implications clearly.
• Most importantly, we tie evaluation to business goals, showing we understand the cost of errors and what metrics matter most in practice.
• This thoughtful approach proves that we’re not just building models—we’re optimizing for meaningful outcomes.

If you want to learn even more from Yayun:

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