Ensemble Machine Learning Techniques 3.2: How Bagging Works

This video tutorial introduces the concept of bagging, an ensemble technique that uses bootstrapping to create multiple sub-samples from a dataset. Models are built on these sub-samples, and their predictions are aggregated to improve accuracy. The video explains the process with a diagram and provides pseudocode for implementing bagging in Python. It concludes with a preview of the next video, which will cover using bagging with SVM for movie rating predictions.

What is the primary purpose of using bootstrapping in bagging?

• To ensure data is evenly distributed

• To reduce the complexity of the model

• To create multiple sub-samples for model training

• To increase the size of the dataset

How are predictions combined in bagging to form a final prediction?

• By using the prediction from the first model

• By averaging predictions for regression and voting for classification

• By taking the median of all predictions

• By selecting the prediction with the highest confidence

What is the role of parallelization in the bagging process?

• To ensure models are trained sequentially

• To increase the accuracy of individual models

• To allow models to learn independently and simultaneously

• To reduce the number of models needed

In the pseudocode for bagging, what is the purpose of the empty list of models?

• To store the final predictions

• To keep track of the original dataset

• To list the errors encountered during training

• To store models as they are built on different samples

What is the final step in the bagging pseudocode implementation?

• Re-sampling the dataset for more models

• Training the models on the entire dataset

• Combining results and making predictions

• Selecting the best model based on accuracy