What does an ROC curve tell you about a model’s performance? What distinguishes the model performance between bagging and boosting?

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Anonymous · Accepted Answer

ROC curve
It’s a graph that shows how well a binary classification model separates two
classes at different cutoff points.

* Y-axis: true positive rate

* X-axis: false positive rate

A better model has a curve that stays closer to the top-left corner.
AUC is just the summary score of that curve — higher is better.

Bagging vs Boosting

Bagging
Builds a bunch of models separately, then combines their results.
Used mainly to make predictions more stable and reduce overfitting.

Example: Random Forest

Boosting
Builds models one by one, where each new model tries to fix the mistakes of the
earlier one.
Used mainly to improve weak models step by step.

Example: XGBoost

What does an ROC curve tell you about a model’s performance? What distinguishes the model performance between bagging and boosting?

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