Parallelisation of ML Algorithms

Parallelisation of ML Algorithms #

Machine-learning algorithms expose different kinds of parallel work. The correct decomposition depends on whether independent work occurs across records, features, trees, clusters, kernel entries, or optimisation steps.

Course coverage:

1. Problem decomposition for parallel machine learning
2. Ensemble methods and XGBoost-style tree ensembles
3. Parallel k-means assignment and centroid reduction
4. Distributed decision trees and random forests
5. Support vector machine parallelisation using block and MapReduce-style computation

Learning Objectives #

By the end of this page, you should be able to:

Ensemble Learning

Ensemble Learning #

Ensemble Learning is a machine learning approach where we combine multiple models to produce a stronger final prediction.

Instead of depending on one model, an ensemble uses a group of models and combines their outputs.

The main idea is simple:

Many weak or moderately good models can work together to produce a better and more stable model.

Key takeaway:
Ensemble Learning improves prediction by combining several models.