Bagging and Boosting in Machine Learning

Last Updated on July 4, 2023 by Mayank Dham

In our daily lives, we frequently employ the Decision Tree Technique to make decisions. Similarly, organizations utilize supervised machine learning techniques like Decision trees to improve decision-making processes and increase their overall surplus and profit.

Ensemble methods merge multiple decision trees to produce more accurate predictive outcomes compared to using a single decision tree alone. The fundamental concept behind an ensemble model is that a collection of weaker learners join forces to create a stronger learner.

Let’s get started with the definition of Bagging and Boosting

What is Bagging in Machine Learning?

In machine learning, bagging (Bootstrap Aggregating) is a technique used to improve the performance and robustness of predictive models. It involves creating multiple subsets of the training data through random sampling with replacement. Each subset is then used to train a separate model, and their predictions are combined to make a final prediction.

The main idea behind bagging is to introduce diversity among the models by exposing them to different subsets of the training data. This helps to reduce overfitting and improve generalization by averaging out the biases and variances of individual models.

Here’s a step-by-step explanation of how bagging works:

• Data Sampling: Random subsets of the training data are created by sampling with replacement. Each subset, known as a bootstrap sample, has the same size as the original training set.

• Model Training: A separate model is trained on each bootstrap sample. The models can be of the same type (using the same learning algorithm) or different types.

• Model Independence: Each model is trained independently of the others, meaning they have no knowledge of each other’s predictions or training process.

• Prediction Combination: During prediction, each individual model makes its own prediction on the test data. The final prediction is typically determined by aggregating the predictions of all models, either through majority voting (for classification tasks) or averaging (for regression tasks).

Example of Bagging in Machine Learning:

Let’s consider an example of bagging using the Random Forest algorithm, which is a popular ensemble method based on bagging.

Suppose we have a dataset of customer information, including features like age, income, and purchasing behavior, and we want to build a predictive model to classify customers as either "churn" or "non-churn" (indicating whether they are likely to leave or stay with a service, for example).

In the bagging process with Random Forest, we follow these steps:

• Data Sampling: Random subsets of the training data are created by sampling with replacement. For instance, we might randomly select 70% of the original data for each bootstrap sample. Each subset will have the same size as the original training set but may contain duplicate instances due to sampling with replacement.

• Model Training: We train a separate decision tree model on each bootstrap sample. Each decision tree is trained on a different subset of the data, and they may have different internal structures due to the randomness introduced by sampling.

• Model Independence: Each decision tree model is trained independently of the others. They have no knowledge of each other’s predictions or training process. Each tree is free to learn different patterns and relationships within the data.

• Prediction Combination: During prediction, each decision tree model makes its own prediction on the test data. For classification tasks, the final prediction can be determined through majority voting. Each tree "votes" for the predicted class, and the class with the most votes becomes the final prediction. Alternatively, for regression tasks, the predictions of all trees can be averaged to obtain the final prediction.

The ensemble of decision trees created through bagging (Random Forest) tends to provide better prediction accuracy and robustness compared to a single decision tree. The individual decision trees may have different strengths and weaknesses, but their combination helps to reduce overfitting and improve generalization performance.

Note that Random Forest is just one example of bagging in machine learning. Bagging can be applied to other algorithms as well, such as boosting methods like AdaBoost or Gradient Boosting, where the focus is on creating an ensemble of models with different weights assigned to them.

What is Boosting in Machine Learning?

Boosting is a machine learning ensemble technique that combines multiple weak or base models to create a strong predictive model. Unlike bagging, which focuses on creating diverse models through parallel training, boosting focuses on sequentially improving the performance of individual models.

The basic idea behind boosting is to train a series of weak models, typically decision trees, in which each subsequent model focuses on correcting the mistakes made by the previous models. In other words, the models are trained iteratively, and each iteration assigns higher weight or importance to the samples that were misclassified by the previous models.

Here’s a high-level explanation of how boosting works:

• Model Training: Initially, the first weak model is trained on the original training data. It may not perform well, as it is considered a weak learner. Weak learners are models that perform slightly better than random guessing.

• Weight Assignment: After the first model is trained, the misclassified samples are given higher weights, while the correctly classified samples are given lower weights. This weight assignment emphasizes the importance of the misclassified samples, encouraging subsequent models to focus on those samples.

• Iterative Training: Subsequent weak models are trained, with each model placing more emphasis on the misclassified samples. The models are trained sequentially, meaning that the training process takes into account the results of previous models.

• Weight Update: The weights of the samples are updated after each iteration based on the performance of the previous models. The misclassified samples receive higher weights, allowing the subsequent models to pay more attention to those samples.

• Prediction Combination: During prediction, the final prediction is determined by combining the predictions of all the weak models. The individual predictions can be weighted according to the performance of the respective models.

The ensemble of weak models created through boosting tends to produce a strong predictive model with improved accuracy. Popular boosting algorithms include AdaBoost (Adaptive Boosting) and Gradient Boosting, with variations like XGBoost and LightGBM.

Boosting is effective when the weak models are simple and have low complexity, and when they can be trained quickly. The iterative nature of boosting helps to reduce bias and improve generalization performance by focusing on difficult-to-predict samples.

Difference between Bagging and Boosting in Machine Learning

Bagging and boosting are both ensemble techniques used in machine learning, but they differ in their approaches to combining multiple models and their focus on reducing different sources of error. Here are the key differences between bagging and boosting:

1. Training Approach:

• Bagging: Bagging involves training multiple models independently and in parallel. Each model is trained on a different subset of the training data obtained through random sampling with replacement. The models are typically of the same type and use the same learning algorithm.

• Boosting: Boosting trains models sequentially, where each subsequent model focuses on correcting the mistakes made by the previous models. Each model is trained on the full training data, but the importance of the samples is adjusted based on their classification performance in the previous iterations.

2. Weighting of Samples:

• Bagging: In bagging, each model is trained on a random subset of the data with replacement, and all samples are equally weighted during training. There is no emphasis on correcting the mistakes made by individual models.

• Boosting: Boosting assigns different weights to the training samples. Initially, all samples have equal weights, but misclassified samples receive higher weights in subsequent iterations, allowing subsequent models to focus more on correcting those mistakes.

3. Model Combination:

• Bagging: In bagging, the predictions of individual models are combined through majority voting (for classification tasks) or averaging (for regression tasks). Each model contributes equally to the final prediction.

• Boosting: In boosting, the predictions of individual models are combined by giving different weights to their predictions. Models with higher performance typically receive higher weights, and the final prediction is obtained by weighted averaging or summing of the predictions.

4. Error Reduction Focus:

• Bagging: Bagging aims to reduce the variance of the models by creating diverse models through random sampling. It helps to improve the stability and generalization of the models by reducing overfitting.

• Boosting: Boosting focuses on reducing the bias of the models by sequentially correcting the mistakes made by previous models. It helps to improve the overall accuracy and predictive power of the ensemble by focusing on difficult-to-predict samples.

Popular algorithms based on bagging include Random Forest, where multiple decision trees are trained independently and combined. On the other hand, popular boosting algorithms include AdaBoost (Adaptive Boosting) and Gradient Boosting, where models are trained sequentially and weighted based on their performance.

Conclusion
Bagging and boosting are both ensemble techniques used in machine learning to improve predictive performance. Bagging focuses on reducing variance by creating diverse models through parallel training and combining their predictions while boosting aims to reduce bias by sequentially training models that correct the mistakes made by previous models. Bagging combines models equally while boosting assigns weights to models based on their performance. Random Forest is a popular example of bagging, while AdaBoost and Gradient Boosting are common boosting algorithms.

FAQs related to Bagging and Boosting in Machine Learning

Q1. Which ensemble technique is better, bagging or boosting?
The choice between bagging and boosting depends on the specific problem and dataset. Bagging is effective when the base models are prone to overfitting while boosting is useful when the base models are too weak and need improvement. It is recommended to experiment with both techniques and select the one that yields better performance for a given task.

Q2. Can bagging and boosting be applied to any machine learning algorithm?
Yes, bagging and boosting can be applied to various machine learning algorithms, including decision trees, neural networks, and support vector machines. However, decision trees are commonly used as weak learners in both bagging (Random Forest) and boosting (AdaBoost, Gradient Boosting) due to their simplicity and interpretability.

Q3. Do bagging and boosting reduce overfitting?
Yes, both bagging and boosting help to reduce overfitting, but through different mechanisms. Bagging reduces overfitting by creating diverse models through random sampling, whereas boosting reduces overfitting by iteratively correcting the mistakes made by previous models and focusing on difficult-to-predict samples.

Q4. Are bagging and boosting suitable for imbalanced datasets?
Both bagging and boosting can handle imbalanced datasets, but their effectiveness may vary. Bagging can help by providing a more balanced representation of the minority class in each subset while boosting can focus on correctly classifying the minority class by assigning higher weights to misclassified samples.

Q5. Can bagging and boosting be used together?
Yes, bagging and boosting can be combined to create an ensemble technique called "bagging with boosting." In this approach, bagging is applied to create diverse models, and then boosting is used to improve their performance further by sequentially training them and adjusting sample weights.