Ensemble Learning

Which of the following are basic ensemble techniques?

• Stacking

• Bagging

• Voting

• XGBoosting

In the context of ensemble learning, what does majority voting mean?

• Selecting the most frequent prediction from multiple models.

• Combining model predictions by averaging the probabilities

• Choosing the prediction with the highest probability.

• Using a weighted sum of predictions from different models

Differentiate basic ensemble techniques from boosting ensemble techniques.

_______is an ensemble learning technique that improves the performance of a machine learning model by sequentially training weak models, with each new model focusing on correcting the errors made by the previous models.

The VotingClassifier with voting = "_____" would classify the sample as "class 1" based on the majority class label.

The first parameter of VotingClassifier and VotingRegressor is

• estimators

• estimator

Voting uses single model with different samples of training datasets while Bagging uses different multiple estimators with same training dataset.

• True

• False

______ allows to use the strength of each individual estimator by using their output as input of a final estimator.

_____classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.

• Gradient Boosting

• AdaBoost

• Voting

• Stacking

Learning rate parameter used in boosting techniques is the learning rate used for the gradient descent iteration.

• True

• False