Ensemble boosting bagging

Ensemble learning is the combination of more than one model when training. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). Bagging and Boosting are ensemble techniques to train multiple models using the same learning algorithm and then taking a call.

With Bagging, we take a dataset and split it into training data and test data. Then we randomly select data to place into the bags and train the model separately.

With Boosting, the emphasis is on selecting data points which give wrong output to improve the accuracy.

You could list some examples of ensemble methods (bagging, boosting, the “bucket of models” method) and demonstrate how they could increase predictive power.

[src] An ensemble is the combination of multiple models to create a single prediction. The key idea for making better predictions is that the models should make different errors. That way the errors of one model will be compensated by the right guesses of the other models and thus the score of the ensemble will be higher.

We need diverse models for creating an ensemble. Diversity can be achieved by:

• Using different ML algorithms. For example, you can combine logistic regression, k-nearest neighbors, and decision trees.

• Using different subsets of the data for training. This is called bagging.

• Giving a different weight to each of the samples of the training set. If this is done iteratively, weighting the samples according to the errors of the ensemble, it’s called boosting. Many winning solutions to data science competitions are ensembles. However, in real-life machine learning projects, engineers need to find a balance between execution time and accuracy.

52) What's the difference between boosting and bagging?

Boosting and bagging are similar, in that they are both ensembling techniques, where a number of weak learners (classifiers/regressors that are barely better than guessing) combine (through averaging or max vote) to create a strong learner that can make accurate predictions. Bagging means that you take bootstrap samples (with replacement) of your data set and each sample trains a (potentially) weak learner. Boosting, on the other hand, uses all data to train each learner, but instances that were misclassified by the previous learners are given more weight so that subsequent learners give more focus to them during training. [src]