bagging and boosting and their difference, what is ensemble models, how to handle overfitting, explain precision recall roc curve

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Explanation of bagging, boosting, ensemble models, handling overfitting, and precision-recall-ROC curve.

Bagging (Bootstrap Aggregating) involves training multiple models on different subsets of the training data and combining their predictions through averaging or voting.
Boosting involves training multiple models sequentially, with each model correcting the errors of its predecessor.
Ensemble models combine multiple individual models to improve overall performance and generalization.
To handle overfitting, techniques like cross-validation, regularization, early stopping, and pruning can be used.
Precision-Recall curve evaluates the trade-off between precision and recall for different thresholds, while ROC curve plots the true positive rate against the false positive rate.

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