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# Understanding Ensemble Techniques!!!

## Content:

1. What are Ensemble Methods?
2. Intuition Behind Ensemble Methods!
3. Different Ensemble Methods
• Bagging
4. →Intuition behind Bagging
• Boosting
5. →Intuition behind Boosting
• Stacking
6. →Intuition behind Stacking
• Bucket of models

## What are Ensemble Methods?

• Ensemble methods are techniques that create multiple models and then combine them to produce improved results.
• This approach allows the production of better predictive performance compared to a single model.
• Ensemble methods usually produce more accurate solutions than a single model would. This has been the case in many machine learning competitions, where the winning solutions used ensemble methods.

#boosting #bagging #ensemble-learning #data-science #machine-learning

## Buddha Community

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In my previous post about ensemble learning, I have explained what is ensemble learning, how it relates with Bias and Variance in machine learning and what are the simple techniques of ensemble learning. If you haven’t read the post, please refer here.

In this post I will cover ensemble learning types, advanced ensemble learning methods — Bagging, Boosting, Stacking and Blending with code samples. At the end I will explain some pros and cons of using ensemble learning.

## Ensemble Learning Types

Ensemble learning methods can be categorized into two groups:

1. Sequential Ensemble Methods

In this method base learners are dependent on the results from previous base learners. Every subsequent base model corrects the prediction made by its predecessor fixing the errors in it. Hence the overall performance can be increased via improving the weight of previous labels.

2. Parallel Ensemble Methods

In this method there is no dependency between the base learners and all base learners execute in parallel and the results of all base models are combined in the end (using averaging for regression and voting for classification problems).

Parallel Ensemble methods are divided in two categories-

**1. Homogeneous Parallel Ensemble Methods- **In this method a single machine learning algorithm is used as a base learner.

2. Heterogeneous Parallel Ensemble Methods- In this method multiple machine learning algorithms are used as base learners.

## Bagging

Bagging or Bootstrap Aggregation is a parallel ensemble learning technique to reduce the variance in the final prediction.

The Bagging process is very similar to averaging, only difference is that bagging uses random sub-samples of the original dataset to train same/multiple models and then combines the prediction, whereas in averaging the same dataset is used to train models. Hence the technique is called Bootstrap Aggregation as it combines both Bootstrapping (or Sampling of data) and Aggregation to form an ensemble model.

#artificial-intelligence #ensemble-learning #ensemble #data-science #machine-learning

1601690400

## Content:

1. What are Ensemble Methods?
2. Intuition Behind Ensemble Methods!
3. Different Ensemble Methods
• Bagging
4. →Intuition behind Bagging
• Boosting
5. →Intuition behind Boosting
• Stacking
6. →Intuition behind Stacking
• Bucket of models

## What are Ensemble Methods?

• Ensemble methods are techniques that create multiple models and then combine them to produce improved results.
• This approach allows the production of better predictive performance compared to a single model.
• Ensemble methods usually produce more accurate solutions than a single model would. This has been the case in many machine learning competitions, where the winning solutions used ensemble methods.

#boosting #bagging #ensemble-learning #data-science #machine-learning

1592679840

## Ensemble Techniques

We know that the decision tree is a very powerful technique, but we can improve it using something called as Ensemble Modeling Technique.
Ensemble basically means ‘together’, so ensemble model is a group of base models working together.
For an Ensemble Model to work better, you should choose different base models, i.e. more different these models are, the better you can combine them.
In this blog I’m going to introduce you to the following Ensemble Techniques:
Bagging
Boosting

#python #data-science #ensemble #towards-data-science #machine-learning

1592580360

## Ensemble Techniques

We know that the decision tree is a very powerful technique, but we can improve it using something called as Ensemble Modeling Technique.
Ensemble basically means ‘together’, so ensemble model is a group of base models working together.
For an Ensemble Model to work better, you should choose different base models, i.e. more different these models are, the better you can combine them.

#python #data-science #ensemble #data-visualization #machine-learning

1620093899

## Ensemble Learning Tutorial | Ensemble Techniques | Machine Learning Training

This Edureka video on Ensemble Learning covers the basics of Ensemble Learning Methods. You will learn about the underlying working of Boosting, Bagging, and Voting algorithms. The video will make you learn how to create a model using Ensemble Learning validate it using cross-validation.

These are the following topics that are covered in this Ensemble Learning Tutorial video:-

• 00:00:00 Introduction
• 00:00:47 What is Ensemble learning?
• 00:01:49 Need For Ensemble Learning
• 00:02:31 Bias - Variance Tradeoff
• 00:04:03 Ensemble Learning Techniques
• 00:09:00 Ensemble Learning In Action

#ensemble #developer #machine-learning