Machine learning: Bagging vs. Boosting

As we all know, ensemble learning combines multiple models to enhance machine learning outcomes. Compared to using a single model, this strategy enables the generation of greater prediction performance. The basic concept is to educate a group of classifiers (experts) and give them a voice. Ensemble learning comes in two flavors: boosting and bagging. Due to the fact that they incorporate several estimates from various models, these two reduce the variation of a single estimate. Therefore, the outcome might be a model with greater stability. Let's quickly comprehend these two terms.

Bagging: This is a homogenous weak learner's model that incorporates individual parallel learnings to calculate the model average.

Boosting: It functions differently from bagging despite being a homogeneous weak learners' model. In this strategy, learners acquire knowledge progressively and adaptively to enhance learning algorithm predictions.

To comprehend the distinction between boosting and bagging, let's examine both of these carefully.


An ensemble meta-algorithm for machine learning called bootstrap aggregation, commonly referred to as "bagging," aims to increase the accuracy and stability of machine learning algorithms used in statistical regression and classification. It brings down the variance and helps prevent overfitting. It is frequently used with decision tree approaches. The model averaging method's special case is bagging.

Detailed Description of the Method

A training set Di of d tuples is chosen from a set D of d tuples at each iteration I using row sampling and a replacement mechanism (there may be repetitive items from different d tuples) (i.e., bootstrap). For every training set D < I a classifier model Mi is subsequently learned. The class prediction of each classifier, Mi, is returned. Assigning the class with the highest votes to X is the bagged classifier M* (unknown sample).

Implementation Steps of Bagging

Step 1: Using equal tuples, multiple subsets of the original data set are constructed by choosing observations and replacing them.

Step 2: A foundation model is built on each of these subsets.

Step 3: Each model is independently and concurrently learned with each training set.

Step 4: The combined algorithms' combined forecasts are used to arrive at the final predictions.

Bagging Example

Decision tree models with increased variance are included in the Random Forest model, which incorporates bagging. Trees are grown using a random feature selection process. Many random trees create a Random Forest.


By combining several weak classifiers, the ensemble modeling technique known as "boosting" aims to create a powerful classifier. It is accomplished by using weak models in series to develop a model. First, a model is created using the training set of data. The second model is then created in an effort to fix the previous model's flaws. Models are added in this manner until the entire training data set is successfully predicted or the optimum number of models is added.

Similarities Between Bagging and Boosting

Both of the widely used approaches, bagging and boosting, are consistently categorized as ensemble methods. We shall discuss their similarities in this section.

● Both ensemble procedures start with a single student and produce N learners.
● Both use random sampling to produce a number of training data sets.
● Both effectively lower variation and offer greater consistency.
● By averaging the N learners, both get to their final decision (or taking the majority of them i.e., Majority Voting).

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