Bagging vs Boosting in Machine Learning

As we know, Ensemble learning helps improve machine learning results by combining several models. This approach allows the production of better predictive performance compared to a single model. Basic idea is to learn a set of classifiers (experts) and to allow them to vote. Bagging and Boosting are two types of Ensemble Learning. These two decrease the variance of a single estimate as they combine several estimates from different models. So the result may be a model with higher stability. Let's understand these two terms in a glimpse.

1. Bagging: It is a homogeneous weak learners’ model that learns from each other independently in parallel and combines them for determining the model average.
2. Boosting: It is also a homogeneous weak learners’ model but works differently from Bagging. In this model, learners learn sequentially and adaptively to improve model predictions of a learning algorithm.

Let’s look at both of them in detail and understand the Difference between Bagging and Boosting.

Bagging

Bootstrap Aggregating, also known as bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It decreases the variance and helps to avoid overfitting. It is usually applied to decision tree methods. Bagging is a special case of the model averaging approach. 

Description of the Technique

Suppose a set D of d tuples, at each iteration i, a training set D_i of d tuples is selected via row sampling with a replacement method (i.e., there can be repetitive elements from different d tuples) from D (i.e., bootstrap). Then a classifier model Mi is learned for each training set D < i. Each classifier M_i returns its class prediction. The bagged classifier M* counts the votes and assigns the class with the most votes to X (unknown sample).

Implementation Steps of Bagging

• Step 1: Multiple subsets are created from the original data set with equal tuples, selecting observations with replacement.
• Step 2: A base model is created on each of these subsets.
• Step 3: Each model is learned in parallel with each training set and independent of each other.
• Step 4: The final predictions are determined by combining the predictions from all the models.

Example of Bagging

The Random Forest model uses Bagging, where decision tree models with higher variance are present. It makes random feature selection to grow trees. Several random trees make a Random Forest.

To read more refer to this article: Bagging classifier

Boosting

Boosting is an ensemble modeling technique designed to create a strong classifier by combining multiple weak classifiers. The process involves building models sequentially, where each new model aims to correct the errors made by the previous ones.

• Initially, a model is built using the training data.
• Subsequent models are then trained to address the mistakes of their predecessors.
• boosting assigns weights to the data points in the original dataset.
• Higher weights: Instances that were misclassified by the previous model receive higher weights.
• Lower weights: Instances that were correctly classified receive lower weights.
• Training on weighted data: The subsequent model learns from the weighted dataset, focusing its attention on harder-to-learn examples (those with higher weights).
• This iterative process continues until:
  • The entire training dataset is accurately predicted, or
  • A predefined maximum number of models is reached.

Boosting Algorithms

There are several boosting algorithms. The original ones, proposed by Robert Schapire and Yoav Freund were not adaptive and could not take full advantage of the weak learners. Schapire and Freund then developed AdaBoost, an adaptive boosting algorithm that won the prestigious Gödel Prize. AdaBoost was the first really successful boosting algorithm developed for the purpose of binary classification. AdaBoost is short for Adaptive Boosting and is a very popular boosting technique that combines multiple “weak classifiers” into a single “strong classifier”.

Algorithm:

1. Initialise the dataset and assign equal weight to each of the data point.
2. Provide this as input to the model and identify the wrongly classified data points.
3. Increase the weight of the wrongly classified data points and decrease the weights of correctly classified data points. And then normalize the weights of all data points.
4. if (got required results)
     Goto step 5
   else
     Goto step 2
5. End

To read more refer to this article: Boosting and AdaBoost in ML

Similarities Between Bagging and Boosting

Bagging and Boosting, both being the commonly used methods, have a universal similarity of being classified as ensemble methods. Here we will explain the similarities between them.

1. Both are ensemble methods to get N learners from 1 learner.
2. Both generate several training data sets by random sampling.
3. In Bagging, the final decision is made by averaging predictions or majority voting. In Boosting, new models focus on correcting previous errors, and the final decision is made by a weighted majority vote.
4. Both are good at reducing variance and provide higher stability.

Differences Between Bagging and Boosting

Differences between bagging, boosting and stacking in Machine Learning

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