Boosting algorithms in machine learning are also known as assembling algorithms. In general boosting algorithms create and train various models where every model tries to overcome the error of the previous model. In general, these models are known as weak learners. So, we can say that boosting algorithms combine weak learners to come up with a strong learning model. In this article, we will discuss what is boosting and why is it so popular. We will also go through some of the popular boosting algorithms in Machine Learning.
Before you start the article it is highly recommended to go through the introduction to Machine Learning to get familiar with Machine learning concepts. If you have missed any tutorials from the ML category, you can find them here. One of the key concepts, that you should have before moving to the boosting algorithms is the basic understanding of decision trees and most of the boosting/resemble algorithms uses decision trees as weak learners.
What are Boosting Algorithms in Machine Learning?
Boosting algorithms are supervised machine learning algorithms, in the sense that they take the training dataset to train the weak learners. It can be used for both classification and regression problems. To construct a more reliable model, it combines a number of weak models, such as decision trees. The several models are trained one after the other, each correcting the errors of the previous model. The combined predictions of all the separate models yield the final results. Additionally, because of its low susceptibility to overfitting, it frequently achieves high levels of accuracy without sacrificing generalizability. Therefore, boosting is a preferred option for many real-world machine-learning applications.
The general idea behind boosting algorithms is that a random sample of data is selected and is fed to the sequence of models where each model tried to overcome or reduce the errors of the previous model.
What is a Weak Learner in Boosting Algorithms?
As we discussed that boosting is a type of machine learning algorithm that does not build just one model, instead it creates multiple models by randomly taking samples from the dataset. These multiple models are known as weak learners. The boosting algorithms build the next model in such a way that it tries to reduce the error created in the first model. In simple words, creating multiple models boosts accuracy as every model tries to perform better than the previous one.
How do the Boosting Algorithms Work?
As we know that the boosting algorithms create multiple weak learners on random distribution of the dataset and then combine all weak learners to create a strong learner. For example, consider the diagram below, where the weak learners try to classify the dataset and then they combine to create a strong learner.
What Makes Boosting Algorithms So Popular?
It is true that boosting algorithms are getting more and more popular day by day. Some of the main reasons for their popularity are mentioned below:
- Algorithms in Boosting are simple to understand and interpret and are designed to learn from errors.
- Boosting algorithms can deal with missing data and don’t require any preprocessing of the data. Additionally, most programming languages come with built-in libraries that can be used to construct performance-tuning boosting algorithms with a variety of parameters.
- Inaccurate or ambiguous findings from machine learning are referred to as bias. Boosting algorithms sequentially combines a number of weak learners, improving observations one at a time. This method aids in lowering the high bias seen in many machine-learning models.
- During training, boosting algorithms give preference to features that improve predicted accuracy. They can aid in the reduction of data attributes and the effective management of massive datasets.
Top Boosting Algorithms in Machine Learning in 2023
As the boosting algorithms are getting more and more popular different research had been done and in recent years, many new powerful boosting algorithms have been introduced. In this section, we will discuss some of these powerful boosting algorithms and in the next articles, we will explain each of these powerful algorithms in more detail and will implement them using Python.
LightGBM Algorithm in Machine Learning
LightGBM is a short form of Light Gradient Boost Machine. It was introduced by Microsoft company and was made publically available in 2016. It creates many weak decision trees and combines those all to form a strong learner model. It automatically handles the NULL values.
Some of the main features of the LightGBM algorithm are:
- It has a faster training speed and high efficiency.
- It uses low memory
- It has better accuracy compared to many other boosting algorithms.
- It supports parallel, distributed, and GPU learning.
- It can easily handle a large set of data.
- It uses a histogram-based algorithm for splitting to create split points
CatBoost Algorithm in Machine Learning
CatBoost algorithm is a gradient boosting algorithm that also creates weak decision trees and then combines those all to come up with a strong learning model. It was developed by Yandex researchers and was made publically available in 2017. It is very powerful in terms of handling categorical values. CatBoost is a short form of Categorical boosting. It is not using the traditional way of encoding categorical values, in fact, it has a unique and smart way of encoding categorical values.
Some of the main features of the CatBoost algorithm are:
- No need for parameter tuning as it gives the best result with the default values of the parameters.
- No need to preprocess non-numeric values as the CatBoost will encode the non-numeric values in a smarter way
- It is fast and GPU scalable.
- It has better accuracy and is much faster for predictions as compared to other boosting algorithms.
- It also handles null values automatically.
- The algorithm uses binary symmetric decision trees.
XGBoost Algorithm in Machine Learning
XGboost is also a boosting algorithm that uses gradient boosting methods. XGBoost is a short form of extreme Gradient boost. Lasso and Ridge Regression regularisation are both used by XGBoost to punish the extremely complex model. Although we can produce distinct tree nodes in parallel using XGboost, but we cannot train several trees in parallel.
Here are some of the main features of the XGBoost algorithm.
- XGBoost can be trained with multiple CPU cores.
- It uses different regularization techniques to reduce overfitting
- It also handles NULL values automatically.
- It can easily detect and learn from the non-linear dataset.
- It uses a cross-validation technique.
- It is available in various programming languages.
Gradient Boosting Algorithm in Machine Learning
The gradient boosting algorithm is a greedy approach algorithm which means it can overfit the dataset quickly if not properly handled. We can use many regularization techniques in order to reduce the risk of overfitting in the Gradient boosting algorithm. In a similar way to another boosting algorithm, it combines the predictions of the weak learner to produce a strong predictive model. Gradient boosting algorithm can be used for predicting not only continuous target variable but also categorical target variable When it is used as a regressor, the cost function is Mean Square Error (MSE) and when it is used as a classifier then the cost function is Log loss.
AdaBoost Algorithm in Machine Learning
Ada boosting is the short form of adaptive boosting. Ada boost mostly uses one-level decision trees as weak learners and combines their predictions to create a strong predictive model. One level decision tree means a decision tree that has only one spit. Such decision trees as known as decision stumps.
Boosting algorithms belong to the supervised learning algorithms that combine many weak learners to create strong predictive models. In most cases, the weak learners are decision stumps or trees. In this article, we discussed boosting algorithms and come across some of the popular boosting algorithms. In the next articles, we will discuss each of these boosting algorithms in detail and will implement them in Python