Bagging and Boosting in Machine Learning

In Machine Learning, you must have encountered numerous techniques that are designed to enhance the performance of your model. Among these techniques, the ensemble learning methods have stood out as efficient and effective. The two most important techniques are bagging and boosting. They have the ability to combine multiple models to achieve better accuracy. In this blog, we are going to take you through these 2 techniques, then highlight their mechanisms, differences, and lastly talk about the applications. So, let’s get started!

Table of Content

• What is Ensemble Learning
• What is Bagging?
• What is Boosting?
• Popular Boosting Algorithms
• Differences between Bagging and Boosting
• Advantages and Disadvantages of Bagging and Boosting
• Conclusion

What is Ensemble Learning?

Ensemble learning is a machine learning technique where multiple models, which are also referred to as “weak learners”, are combined to create a structure that is more robust and accurate. This technique helps you to nullify the errors that each model makes. This leads to better performance of the model, especially for new or unseen data. This technique is useful in reducing overfitting and helps you to enhance the model’s stability.

What is Bagging?

Bagging is also called Bootstrap Aggregation. It is an ensemble technique that focuses on the reduction of variance to boost the performance of the model. This technique involves generating multiple versions of a predictor. You can do this by creating random subsets of the original dataset through bootstrapping. You have to train a separate model with each subset of the dataset, and then you get the final prediction of the model by calculating the average of the outputs (for regression tasks) or you can also use the majority voting technique (for classification tasks) of all the models. This technique is effective with the high-variance models, like decision trees. This is because it helps to stabilize the predictions of the model and reduce overfitting.

1. How Bagging Works?

The working process of bagging involves generating multiple bootstrapped datasets (random samples which were taken as a replacement from the original dataset) and training of separate models on each of these datasets. Since each model sees a slightly different data, each of these models make slightly different predictions. When you combine all their predictions either by the process of voting (for classification tasks) or averaging (regression tasks), you get a result that is more accurate and stable. This technique helps you to reduce the randomness and overfitting that a model could have.

2. Steps of Bagging

Now we will talk about the steps which are involved in the working process of bagging.

2.1. Data Sampling

As the first step, you have to create multiple smaller datasets from the original dataset. You can do this by picking the data points randomly, and it is fine if you pick some of the data points more than once. This process is called bootstrapping. Each of the new datasets will be a little different from the other, and some may even have duplicate entries. You can use each of these datasets to train a separate model.

2.2. Model Training

In the second step, you have to train a different model on each of the bootstrapped datasets you have created. Usually, you can use the same type of model for all of them, like the decision trees, but since a different version of the data is seen by each model, they all learn from the data in a slightly different ways. This variety will help you to improve the overall prediction of the model when the results are combined.

2.3. Aggregation

Once all of your models have made their predictions, you can combine them to get the final result. 

• If you are working on a classification problem (like predicting messages as “spam” or “not spam”), you have to look at what most of the models predict and go with the majority vote.
• If it is a regression problem (like you have to predict a price or a number), you have to calculate the average of all the predictions made by the models to get the final answer.

3. Example Algorithm: Random Forest

A good example of bagging is the Random Forest Algorithm. Here, you have to build many decision trees, and each one should be trained on a different random sample of the original data. Once you have trained the trees, you have to combine the predictions. You can do this by either taking a majority vote (for classification tasks) or by calculating the average of their results (for regression tasks). This technique works well because decision trees can be a powerful tool, but sometimes, they can be unstable as well. By using bagging, Random Forest helps you to make the model accurate, stable, and less likely to undergo overfitting.

What is Boosting?

Boosting is another ensemble technique like Bagging. Its focus is to convert weak learners into strong ones to create a strong predictive model. You can do this by giving importance to the error corrections of the previous models. It operates in a sequential format, where each model is trained to point out the mistakes of the previous model.

During this process,  instances that were classified in a wrong way are assigned higher weights. This forces models to focus more on the challenging cases. The model that you have finally obtained is used to aggregate the outputs of all individual models through a weighted sum. This gives the best prediction of the model. Boosting is proficient at reducing the model biases and enhancing the accuracy of the model.

1. How Boosting Works

The working process of Boosting involves building models one after another, they are not built all at once. After you have trained the first model, you point out where the model makes mistakes. Then you have to train the next model, which focuses on fixing those mistakes. 

Each new model attempts to do better by learning from the mistakes made by the previous models. By repeating this process several times, the final model becomes more accurate, as it is constantly improving step by step.

2. Steps of Boosting

Now, we will discuss the steps involved in the process of boosting.

1. Model initialization: The process of boosting starts by training a simple model, like a small decision tree, on the entire dataset. The first model is not very accurate, but it is okay. It is just the starting point, and the main aim is to keep moving from this step.

2. Weight adjustment: After the model makes its predictions, you have to point out the examples it has gotten wrong. This algorithm gives more priority to the mistakes. This means that the next model will also give extra priority to those hard examples and try to get them right.

3. Model Combination: As you go on building the models, you have to add each of those models to a group called an ensemble. Then you have to combine all the predictions to make the final decision.

• For classification tasks, you have to give more weights to the models to perform better, and then choose the option that has the most weighted values.
• For regression tasks, you have to take the average of all the predictions made by the model to get the final results.

Popular Boosting Algorithms

In this topic, we will discuss the popular boosting algorithms. They include the following:

1. AdaBoost (Adaptive Boosting)

AdaBoost is one of the earliest and most popular algorithms. You can implement this by training a simple model on the entire dataset. It then prioritises incorrect examples for the next model. This helps it to focus harder on those examples and make them correct. This process goes on repeating until each new model learns from the mistakes made by the previous model. 

Once you have trained all the models, AdaBoost gives more weight to the models that performed better and less weight to the ones that didn’t. The final prediction is a weighted mix of all the predictions made by the models, where the models that performed better have a bigger contribution to the outcome.

2. Gradient Boosting Machines (GBM)

Gradient Boosting is the upgraded version of AdaBoost. It takes a smarter approach by using the gradient descent, instead of focusing on the examples that have gone wrong.

After the model makes predictions, you have to look for the errors made by the model. Then you have to train the model specifically for fixing those errors. Here, each of the new models tries to correct what was missed by the previous models. This keeps continuing until the overall performance of the model becomes even more accurate.

3. XGBoost (Extreme Gradient Boosting)

XGBoost is a faster and more powerful version of Gradient Boosting. It is the most popular boosting algorithm because it works well with several problems. The thing that makes XGBoost special is that it is designed to be both efficient and accurate. It also has a few extra sets of features that make it better. They are mentioned below. 

• It also uses parallel processing. This means that it can train models much faster by doing multiple tasks at a time.
• It includes regularization. This helps you to prevent your model from overfitting.

4. LightGBM

LightGBM is another version of the gradient boosting algorithm. It is designed to work really well with large datasets. The thing that makes LighGBM stand out is that it trains faster and uses less memory than other boosting algorithms. It is done by using a couple of smart techniques.

• It grows trees in a leaf-wise way instead of level-by-level. This means that it focuses more on the parts of the data that reduce the most errors. This makes the algorithm more efficient.
• It also uses a histogram-based learning technique. This helps to group data into buckets, which helps to speed up the training process and save memory.

Differences between Bagging and Boosting

The differences between Bagging and Boosting are given below.

Advantages and Disadvantages of Bagging and Boosting

1. Advantages of Bagging

• Reduces Overfitting: Bagging helps you to prevent overfitting by calculating the average of the predictions of the multiple models.
• Improves Stability: It helps to make the model more stable by reducing the effect of random noise in the data.
• Parallel Training: Since the models are trained independently, bagging can be parallelized easily for faster training.
• Works well with High-Variance: Bagging can be effective with models containing decision trees that tend to overfit easily.
• Easy to Implement: Bagging is a conceptually simple technique ns can be supported in most ML libraries.

2. Disadvantages of Bagging

• It is less effective at reducing bias: Bagging is not useful for reducing bias if the model is too simple.
• It may require more resources: Training models can increase the computational cost and memory usage.
• It is less interpretable: Since the final model is an average of many, it is harder to interpret than a single model.
• Does not focus on hard cases: Each model sees random samples, and they don’t specifically learn from the errors in previous ones.

3. Advantages of Boosting

• Reduces Bias and Variance: Boosting helps to improve the accuracy of the model by focusing on both the bias and variance of the model.
• Learns from mistakes: Each new model is responsible for correcting the errors of the previous ones. This leads to continuous improvement in the model performance.
• Works Well with Weak Learners: Even simple models are able to perform very well when the boosting algorithm is implemented in them.
• High Predictive Power: Boosting often leads to better performance than many other algorithms, especially in the case of complex data.
• Flexibility: It works well for both classification and regression tasks.

4. Disadvantages of Boosting

• Risk of Overfitting: Boosting can lead to overfitting, especially when the model is too complex or is trained too long without regularization.
• Sequential Training is Slower: Since models are built one after another, it takes longer to train than bagging.
• Sensitive to Noisy Data: Boosting can be affected because it pays too much attention to outliers or noise in the data.
• Harder to Tune: Boosting often requires careful tuning of hyperparameters like learning rate and number of iterations.
• Less Transparent: Like the bagging algorithm, the final model in the boosting algorithm is also hard to interpret due to its ensemble nature.

Conclusion

In Machine Learning, these ensemble methods, like Bagging and Boosting, serve as powerful tools for enhancing the performance of the model. By understanding their mechanisms and applications, you can select the appropriate technique to address specific challenges in your model. This leads to the formation of more accurate and reliable predictive models.

If you are interested in learning about these techniques, then we recommend you to check our Machine Learning Course.