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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Exploring Ensemble Learning in Machine Learning World!

Shanthababu Pandian
Shanthababu Pandian Last Updated : 20 Nov, 2024
6 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Before getting starts with “Ensemble Techniques” in Machine Learning (ML) space. Let try to understand the meaning of the Ensemble in simple terminology, so that we could correlate it with ML. Are you ready!

Ensemble – “A group of items viewed as a whole rather than individually.”. In the below picture a group of people who work or perform together, from the same instrument family.

The term is widely used in the musical world and best fit too. Just find the real meaning of Ensemble in the dictionary, you can understand it better.

Source

Ensemble in Machine Learning

Now let’s compare this within our Machine Learning world. As a group of people in orchestra are performing the synchronize and giving best performance out of them, likewise ensemble methods are techniques, that create multiple models and then combine them to produce an improved version of results for our model. It helps us to produces more quality and accurate solutions than a single model.

          Ensemble Learning:  is a group of predictors that are trained and used for predictions in ML

 

Simple Understanding of Ensemble Learning

A simple representation of this technique is below.

Simple representation of Ensemble Learning
Simple representation

Here a models A, B, C, and D could be anyone of the following Machining Learning algorithm as we are familiar with Logistic Regression, Decision Tree, Random Forest, SVM, etc. It makes sense to us that a group of learners (weak) comes together and to form a stronger community learner, as a result of increasing the accuracy of any Machine Learning model.

 

  Ensemble Exercise

Ensemble Exercise
Ensemble Exercise

A quick flashback, hopes you all know the concept behind the Random Forest model. I mean arriving at the predictions of all individual trees of the decision tree model, taking votes from this group, and predicting the class. This is one way of the ensemble, through the decision tree model and so-called Random Forest.

 

Random Forest ensemble learning
Random Forest

Categories of Ensemble Learning

A strong recommendation for Ensemble Learning as follows, If the predictors are independent, we could train them using a series of different algorithms. So, this approach would increase the chances of different types of errors, improving accuracy.

Let’s discuss the available Ensemble Learning. Before that let see high-level categories as a SEQUENTIAL and PARALLEL ensemble.

      SEQUENTIAL ENSEMBLE TECHNIQUES: Sequentially generate the base learners as shown in the below diagram. In this sequential generation, the base learners are encouraging the dependency between the base learners. End of the day the performance of the model is then improved by assigning higher weights to previously misstated learners in the group.

SEQUENTIAL ENSEMBLE TECHNIQUES
SEQUENTIAL ENSEMBLE TECHNIQUES

 

        PARALLEL ENSEMBLE TECHNIQUES: In which the base learners are generated in parallel as like in Random Forest, as we know that the collection of decision tree makes random forest with base learners to encourage independence between the other base learners. here the error can be reduced dramatically by averaging.

One step ahead, we can classify ensemble as HOMOGENEOUS and HETEROGENEOUS ENSEMBLE based on the building the model

      HOMOGENEOUS ENSEMBLE is a collection of classifiers of the same type, built upon a different subset of data as we use to do in the Random Forest model.

  1. Data 1 <> Data 2 <> Data 3 …… Data n
  2. Examples: Bagging and Boosting

      HETEROGENEOUS ENSEMBLE is a set of classifiers of different types, built upon the same data.

  1. Data 1 = Data 2 = Data 3 …… Data n
  2. Examples: Stacking
HETEROGENEOUS ENSEMBLE
HETEROGENEOUS ENSEMBLE
 

Comparison of Homogeneous and Heterogeneous Ensemble

Homogeneous and Heterogeneous Ensemble

So far, we have discussed different category

different category
Different Category

Now, will move on to types in details,

 

Types of Ensemble methods

      Bagging: Which reduces the variance to a large extent level and eventually increases accuracy. So, it is eliminating overfitting, which was a big challenge in many predictive models. This is often considered as a homogeneous weak learner and learns them independently from each other in parallel and combines them following by averaging process

Further classified into two types as Bootstrapping and Aggregation.

  1. The key advantage of the method is the weak base learners are combined to form a single strong learner, which is more stable than single learners.
  2. As mentioned earlier, it also eliminates any variance and reducing the overfitting of models.
  3. Expensive computationally is the major challenge in this method.
Bagging

                  Bootstrapping is a sampling-based technique, where the samples are derived from the whole population using the replacement procedure. This replacement method helps make the selection procedure randomized. The base learning algorithm is run on the samples to complete the procedure.

              Aggregation is based on the probability bootstrapping procedures (Shown in the above diagram) or based on all outcomes of the predictive models.

Boosting: Most widely used Ensemble method and powerful. Actually, speaking this was designed for classification problems and further extended to regression problem statement too. This is basically a combination of 3+ weak algorithms (learners) to generate a strong learner.

Available Boosting algorithms are AdaBoost, Gradient Boosting Machine (GBM), XGBoost, and Light GBM

The situation for Boosting, Let’s have a situation that first model predicted erroneous and followed by the second model as well predicted inaccurate, then will analyze these results by combining and extract the better prediction. This is nothing but Boosting overall results. Hence Boosting proves the ensemble core concepts as combining a weak learner into a stronger one.

Boosting

Stacking [Stacked generalization]: Stacking is based on the normal way of training a model and perform the aggregation, as we have shown in the diagram – HETEROGENEOUS ENSEMBLE. In which we have two layers one is called the Base-model and Meta-model. The base-level models are trained based on a complete training set, then the meta-model is trained on the outputs of the base-level model, like features.

Since the base level often consists of different learning algorithms and therefore stacking ensembles are often called heterogeneous.

So, we understood different types of Ensemble Techniques!

ensemble

Here the overall takeaway for you all,

  • The target for the ensemble techniques is used to improving predictability in models by combining several models or training different sets of data with a single model to make one very strong model.
  • In the ensemble technique, we used to apply either order to decrease variance, bias, or improve predictions.
  • The most popular ensemble methods are in practice are Boosting, Bagging, And Stacking.
  • Ensemble methods are suitable for regression and classification problems, where they are used to reduce variance and bias to enhance the accuracy of models.

Guys! Thanks for reading this article will discuss more in the upcoming blogs!

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

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Shanthababu Pandian
Shanthababu Pandian

Shanthababu Pandian has over 23 years of IT experience, specializing in data architecting, engineering, analytics, DQ&G, data science, ML, and Gen AI. He holds a BE in electronics and communication engineering and three Master’s degrees (M.Tech, MBA, M.S.) from a prestigious Indian university. He has completed postgraduate programs in AIML from the University of Texas and data science from IIT Guwahati. He is a director of data and AI in London, UK, leading data-driven transformation programs focusing on team building and nurturing AIML and Gen AI. He helps global clients achieve business value through scalable data engineering and AI technologies. He is also a national and international speaker, author, technical reviewer, and blogger.

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Basics of Ensemble Learning Explained in Simple English
Basics of Ensemble Learning Explained in Simple English
Bagging, Boosting and Stacking: Ensemble Learning in ML Models
Bagging, Boosting and Stacking: Ensemble Learning in ML Models

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