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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