Ensemble Learning

Jul 2015

13 Mon

14 Tue

15 Wed

16 Thu 08:30 AM – 06:35 PM IST

17 Fri 08:30 AM – 06:30 PM IST

18 Sat 09:00 AM – 06:30 PM IST

19 Sun

NIMHANS Convention center

All submissions

Previous Next

Ensemble Learning

Submitted Jun 14, 2015

Section: Full Talk Technical level: Beginner

To understand most basic and convenient approaches of ensembling

Outline

Ensemble learning is all about combining predictions from different machine learning techniques in order to create a stronger overall prediction. For example, the predictions of a random forest, a support vector machine, and a simple linear model may be combined to create a stronger final prediction set. The key to creating a powerful ensemble is model diversity. An ensemble with models that are very similar in nature will perform lower than a more diverse model set.

Ensemble learning can be broken down into two tasks: developing a population of base learners from the training data, and then combining them to form the composite predictor.In this talk I would like to give a basic overview of ensemble learning. And discuss about building an ensemble model by conducting a regularized and supervised search in a high-dimensional space of weak learners.

No wonder most of the winning submissions in Kaggle are ensemble models.