Name: Model Training
Uploaded: 2025-06-02T22:02:53+00:00
Duration: 1 min 25 s
Description: In this segment, we cover model training, a phase where many candidates fall into autopilot

Model Training

In this segment, we cover **model training**, a phase where many candidates fall into autopilot. To stand out, we need to show thoughtfulness, adaptability, and a clear connection between our choices and the problem at hand.

- **Mediocre performance** involves picking a default model (e.g., logistic regression or random forest), using out-of-the-box settings with no tuning or justification.
- **Strong candidates** choose models deliberately, explaining how their selection fits the **data characteristics, business goals, or system constraints**.
- We improve performance through **hyperparameter tuning**, using techniques like grid search or informed manual adjustments based on EDA insights.
- When appropriate, we apply **ensemble methods** (e.g., boosting, bagging, stacking) to push performance further.
- Most importantly, we demonstrate that our modeling choices are **intentional and grounded in context**, not just rote or random experimentation.

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In this segment, we cover model training, a phase where many candidates fall into autopilot

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.