Bagging and Boosting in Machine Learning - Key Techniques in Ensemble Method

Machine learning technology is a boon in developing AI models, but what makes machine learning an accurate and reliable method is Ensemble learning. Ensemble learning improves the performance of machine learning models.

Ensemble learning combines multiple machine learning models to achieve more accurate models. Bagging and Boosting in machine learning are the most popular ensemble method techniques; every tech professional must know them.

Bagging and Boosting are essential techniques of the ensemble method. Though both techniques are different, it is slightly difficult to distinguish between them. Each technique has a different approach to model accuracy.

Before learning about the difference between Bagging and Boosting techniques, we must understand Ensemble Learning.

Ensemble Learning

The machine learning models that perform poorly are known as weak learners. Weak learners have low prediction accuracy as they have either high bias or high variance.

A highly biased ML model doesn’t learn efficiently from data and makes irrelevant predictions. A model with high variance learns too much from the data, and the varying nature of data at each data point makes it predict inaccurate results.

Ensemble learning integrates multiple weak learners to obtain more balanced and better-performing models. Bagging and Boosting are used to reduce variance and bias in models respectively.

Bagging in Machine Learning

Bagging is a machine learning ensemble algorithm that combines weak learners of high variance. It consists of two stages: Bootstrapping and Aggregating; hence, bagging is also called Bootstrap Aggregation. Bagging is usually used to reduce the variance of decision trees.

Bootstrapping - This step involves resampling datasets from initial datasets called bootstraps. Each dataset can be resampled multiple times to train weak learners.

Aggregating - The weak learners are trained independently and make individual predictions. All these individual predictions are combined to obtain an accurate and final prediction.

Boosting in Machine Learning

Boosting is used to combine weak learners with high bias. It focuses on developing models having lower bias than individual models. A data subset is used to train the first model and then the second model is developed based on correcting errors in the first model. The subsequent models are based on building accurate models from previous ones.

It has a special function called weighted averaging where the models are allotted different weights based on their predictive power. The learning model with the highest predictive power is given the most priority.

Bagging vs Boosting in Machine Learning

Bagging

Boosting

The training data subsets are drawn randomly from the overall training data

Each new model consists of misinterpreted data from the previous model

Bagging involves combining the same type of predictions

Boosting involves combining different types of predictions

It focuses on reducing variance, not bias

It aims to reduce bias, not variance

Each learning model is provided with equal weightage.

Learning models are weighted based on their performance

Models are developed independently

The performance of the subsequent model is dependent on the previous model

In bagging the models are trained parallelly

In boosting the models are trained in series

Bagging is applied for overfitting models

Boosting is applied for underfit models

The above are the differences between bagging and boosting but they do share some similarities:-

Both ensemble techniques focus on building a stable machine-learning model.

The final prediction is based on combining N learners in both techniques.

Both are used in solving classification and regression problems.

Where to Learn Machine Learning?

In the growing world of data, machine-learning techniques are significant in building accurate models. An industry-oriented machine learning training will help you understand the practical applications of machine learning.

If you are planning to advance your learning on data science and machine learning projects, you can check out Advanced AI and ML Course where you can work on capstone projects and get hands-on experience. All these projects can be surely beneficial in providing a great career understanding of a particular subject.