Extreme Gradient Boosting
Table Of Contents:
- Evolution Of Tree Algorithms.
- What is XGBoost, And Why Is It So Popular?
- What Are The Features Supported By XGBoost?
- Installation of XGBoost.
- Should we use XG-Boost All The Time?
- Hyper-Parameters Involved In XG-Boost.
(1) Evolution Of Tree Algorithm
- Artificial neural networks and deep learning lead the market for unstructured data like images, audio, and texts.
- At the same time, when we talk about small or medium-level structured data, tree-based algorithms dominate the market.
- And when we say tree, it all starts with the basic building block, i.e., Decision Trees DTs were able to solve both classification and regression problems but suffered from overfitting issues quickly.
- To tackle this, we ensembled multiple DTs with slight modifications in data formation. It created Bagging and Random Forest algorithms.
- After that, researchers thought that ensembling trees randomly was time-consuming and computationally inefficient. Why not build trees sequentially and improve over those parts where previous trees failed?
- That’s where boosting came into the picture. Later these boosting algorithms started utilizing the gradient descent algorithm to form trees sequentially and minimize the error in predictions; hence these algorithms are called Gradient Boosting.
- Later, researchers proposed model, algorithmic, and hardware optimizations to further improve the Gradient-boosting algorithms’ performance. The combination of all these optimizations over Gradient boosting is known as XG-Boost, and we will be discussing them in this article.
(2) What Is XGBoost?
XGBoost, also known as Extreme Gradient Boosting, is a supervised learning technique that uses an ensemble approach based on the Gradient boosting algorithm. It is a scalable end-to-end tree boosting system, widely used by data scientists to achieve state-of-the-art results on many machine learning challenges. It can solve both classification and regression problems and achieve better results with minimal effort.
The initial version of this algorithm was implemented using the Gradient Boosting machines. Later after making this work an open-source, a large community of data scientists started contributing to the XGBoost projects and improved this algorithm further. With the help of such a great community, XGBoost has become a software library and can directly be installed into our systems. It supports various interfaces, including Python, R, C++, Julia, and Java. So let’s first install this library and then learn what features it does provide.
(3) Installation Of XGBoost?
- There is official documentation as an installation guide for XGBoost on the XGBoost installation guide. For XGboost in python, Python Package Introduction (PyPI) is the best to start. We can install XGBoost using pip as:
sudo pip3 install xgboost- We know that installing an additional package is always an overhead thing and decreases popularity until it provides something great.
- It’s the same as getting the payment option on WhatsApp; it will be more convenient than using an additional Google Pay application until google pay provides some advanced features.
- Still, XG-Boost manages to give us something exciting, and people do not hesitate to install a different package. So let’s see what features it does provide.
(4) Feature Supported By XGBoost?
Regularized Learning Objective: XGBoost uses a regularized learning objective that combines both a loss function and a regularization term. The loss function measures the discrepancy between predicted and actual values, while the regularization term helps prevent overfitting by adding penalty terms to the objective function.
Gradient-based Optimization: XGBoost optimizes the objective function using gradient-based optimization techniques, such as gradient descent. This allows it to efficiently search for the optimal weak learner at each boosting iteration.
Tree Ensembles: XGBoost employs an ensemble of decision trees as weak learners. Each decision tree is constructed sequentially, with subsequent trees aiming to correct the errors made by the previous ones. XGBoost supports both regression and classification tasks.
Column Block for Sparse Data: XGBoost implements a column block for handling sparse data efficiently. It compresses the sparse input matrix and optimizes memory usage and computation speed, making it well-suited for datasets with a large number of features.
Parallelization and Distributed Computing: XGBoost is designed to leverage parallel computing capabilities. It supports parallel tree construction, which speeds up training on multi-core CPUs. Additionally, XGBoost can be distributed across multiple machines for training on large-scale datasets.
Handling Missing Values: XGBoost has built-in handling for missing values. It automatically learns how to handle missing values during the tree construction process, reducing the need for explicit imputation techniques.
Early Stopping: XGBoost provides early stopping functionality to prevent overfitting. It allows you to monitor the performance on a validation set during training and stop the training process when the performance starts to deteriorate.
Feature Importance: XGBoost calculates feature importance scores based on how frequently a feature is used in the ensemble of trees and how much it contributes to reducing the loss function. These scores can help identify the most influential features in the dataset.
- XGBoost has become a popular choice for various machine-learning tasks, including regression, classification, and ranking. It is known for its excellent predictive performance, scalability, and flexibility.
- The XGBoost library is available in several programming languages, including Python, R, Java, and Scala, making it accessible to a wide range of users and developers.
(5) Should We Always Use XG-Boost If This Is So Effective?
Indeed, XG-Boost’s support is exciting and widely configurable, yet we can not say that it will always work best. There is nothing like one algorithm for all kinds of solutions in Machine Learning.
It is always advisable to try out different algorithms and then decide which works best per our requirements. Sometimes accuracy is not the sole requirement that we expect from our machine learning model.
We also want a decent amount of explainability, lesser computational complexity, and ease in deployment. These factors also help in selecting the best model for our requirements.
Before ending our discussion, let’s understand one important thing, i.e., what are the hyperparameters involved in this algorithm that can be tuned to extract the best out of it.
(6) Hyperparameters Involved In The XG-Boost Algorithm
learning_rate(oreta): Controls the learning rate or shrinkage parameter, which determines the impact of each weak learner on the final prediction. A smaller learning rate makes the algorithm more conservative, but it may require more iterations to converge.max_depth: Specifies the maximum depth of each decision tree in the ensemble. A larger value allows more complex interactions to be captured but increases the risk of overfitting.subsample: Determines the fraction of instances to be sampled for each tree. It controls the randomness of the training process and helps prevent overfitting. Values less than 1.0 introduce stochasticity into the algorithm.colsample_bytree: Controls the fraction of features (columns) to be randomly sampled for each tree. Similar tosubsample, it adds randomness and helps reduce overfitting.n_estimators: Specifies the number of weak learners (decision trees) to be included in the ensemble. Increasing the number of estimators can improve performance, but it also increases training time.gamma: Specifies the minimum loss reduction required to split a node further. A higher value makes the algorithm more conservative and reduces overfitting.lambda(orreg_lambda): Controls L2 regularization term on weights. It adds a penalty to the loss function for large weights, helping to prevent overfitting.alpha(orreg_alpha): Controls L1 regularization term on weights. It encourages sparsity in the model by pushing some weights to exactly zero.min_child_weight: Specifies the minimum sum of instance weights required in a child node to continue splitting. It helps control the tree’s complexity and can prevent overfitting.early_stopping_rounds: Allows early stopping based on a validation set. Training will stop if the evaluation metric does not improve for a specified number of rounds.