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Table of contents
1.
Introduction
2.
Types of Ensemble Classification
2.1.
Bagging
2.2.
Boosting
2.2.1.
How is boosting different from bagging?
2.3.
Stacking
3.
What problems does ensemble classification solve?
4.
FAQs
5.
Key Takeaways
Last Updated: Mar 27, 2024

Ensemble Classification

Author Tashmit
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Introduction

The ensemble is a Latin word derived from the union of parts. Ensemble classification is responsible for combining multiple models. This method trains various models with different datasets and gets the output. Ensemble techniques are of three types: bagging, boosting, and stacking. Ensemble classification is responsible for improving the machine learning outputs by combining weak learner models into one robust model. This allows better predictive outcomes compared to the results of a single model.

Source: Link 

Types of Ensemble Classification

Ensemble classification is of three types:

  1. Bagging
  2. Boosting
  3. Stacking

Source: Link

Let us study them in detail:

Bagging

The first type of ensemble classification is bagging. Another name for bagging classification is bootstrap aggregation used for classification and regression problems. The original dataset is row-wise resampled and trained against multiple decision trees in bagging. Then the final prediction is based on the voting of all the models' predictions combined. Its main focus is to combine the outputs of various weak learners into a single strong learner model.

The most common application of Bagging classification is Random Forest.

Source: Link

In a random forest, the dataset is resampled and given to various decision trees to predict. The majority voting system generates the final output. 

Boosting

Boosting is another type of ensemble classification. It is a self-learning algorithm that learns by allocating weights for various features in the data. This technique initially begins with equal importance, but every model is assigned a weight based on the previous model's performance after every base model. 

Source: Link

The most common use of this technique is in 

  • AdaBoost model
  • XGboost model
  • LightGBM model
  • Gradient boosting model

How is boosting different from bagging?

Bagging classifier helps reduce the prediction variance by creating resampled data for training various decision trees. On the other hand, boosting is a sequential technique that adjusts the weight of current observation based on the previous classification. 

Stacking

The next type of ensemble classification is stacking. This is a method in which a single training dataset is trained on multiple models. The training dataset is divided using the k-fold algorithm, and the final model is built. In stacking, each model indicates a different learning algorithm. The predictions made by these models are known as the first level predictions, i.e., the base model predictions, and these predictions are further used for the second level training, i.e., the meta-model training.

Source: Link

How is stacking different from bagging and boosting?

The main classification between the three categories is that bagging and boosting techniques are homogeneous, i.e., the models used for classification are the same, e.g., decision trees. While in stacking, the models used for classification can be different, like SVM, Logistic Regression, etc. 

What problems does ensemble classification solve?

  • Computational Issues: It comes into play when the learning algorithm cannot guarantee to find the best prediction.
  • Representational Issues: It comes to light when the prediction space does not better approximate the target class.
  • Statistical Issues: This error arises when the hypothesis space is quite ample for the amount of data available.

FAQs

  1. What is the variation between blending and stacking?
    The significant difference between stacking and blending is that stacking uses layer-by-layer prediction or meta-model predictions to make the final output. In comparison, blending uses a 10%-20% dataset to train the next layer.
     
  2. What is an effortless ensemble classifier?
    An easy ensemble classification creates a balanced data sample of a training dataset by selecting all the examples from the minority class and a subset from the majority class. 
     
  3. Why do we need ensemble classification methods?
    There are two primary reasons to use an ensemble classification over a single model classification: 
    → Performance: An ensemble classification can make better predictions and perform better than any single classification model. 
    → Robustness: An ensemble classification reduces the spread or dispersion of the projections and model performance.

Key Takeaways

Ensemble classification is a technique of generating multiple base classification models with the help of which the final prediction is better and mor4e accurate. This article studied ensemble classification, its various types, and problems that ensemble classification can solve. Do you want to build a career in Data Science? Check out our industry-oriented machine learning course curated by our faculty from Stanford University and Industry experts.

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