Model Evaluation and Optimization

Learning how to evaluate model performance using metrics such as accuracy, precision, recall, and AUC-ROC curve, as well as techniques for hyperparameter tuning and optimization, is vital for developing effective machine learning models.

Learning how to evaluate model performance using metrics such as accuracy, precision, recall, and AUC-ROC curve, as well as techniques for hyperparameter tuning and optimization, is vital for developing effective machine learning models.

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Rutika Bhoir
Grad Student at Umass Amherst

Okay, real talk—I didn’t even know about techniques like Grid Search when I first started. I would literally test random combinations of hyperparameters like, “uhh let’s try learning rate = 0.001 and maybe 0.01 too?” and let the code run forever. No strategy. Just vibes. I’m still learning this stuff properly—hyperparameter tuning, cross-validation, performance metrics—but what I have learned so far is that just building a model isn’t enough. You need to evaluate it intentionally. I’m beginning to understand how things like accuracy can be misleading in imbalanced datasets, and how precision, recall, F1-score, and AUC-ROC tell a much more nuanced story. GridSearchCV and tools like RandomizedSearchCV are slowly becoming less intimidating. I still make mistakes (a lot), but I now pause and ask: “Am I evaluating this right? Am I tuning this smartly? Or am I just hoping for the best again?” And honestly, I think that’s what growth in ML looks like—learning to ask better questions and knowing what you don’t know yet.

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