Bias refers to the error introduced by approximating a real-world problem with a simplified model. While variance refers to the model’s sensitivity to fluctuations in the training data.
Ensemble learning methods are a ML technique that combines predictions from multiple models to create a more accurate prediction. There are three main categories of ensemble methods: bagging, boosting, and stacking.
– Accuracy (RMSE/MAE): Measures how well predicted ratings match actual ratings. Good for absolute rating prediction. Ranking (MAP/NDCG): Evaluates how well the most relevant items are ranked at the top of recommendations. Better for item ordering.
The ROC curve is a graph for binary classifiers that shows the trade-off between catching true positives (benefits) vs. false positives (costs) as a classification threshold varies. A perfect classifier lands in the top left corner.