Name: Evaluation
Uploaded: 2025-06-03T12:57:32+00:00
Duration: 1 min 40 s
Description: In this section, we explore model evaluation, a phase that’s often underestimated but critically important

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.

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In this section, we explore model evaluation, a phase that’s often underestimated but critically important

Tech has one of the highest rates of job switching, making this skill incredibly high-leverage and vital to master. Understand what it takes to convince companies you are strongly competent.