What is XGBoost Algorithm?

XGBoost is a machine learning algorithm that belongs to the ensemble learning category, specifically the gradient boosting framework. It utilizes decision trees as base learners and employs regularization techniques to enhance model generalization. XGBoost is famous for its computational efficiency, offering efficient processing, insightful feature importance analysis, and seamless handling of missing values. It’s the go-to algorithm for a wide range of tasks, including regression, classification, and ranking. In this article, we will give you an overview of XGBoost model, along with a use-case!

In this article, you will learn about the XGBoost algorithm. We will explain how the XGBoost classifier works and how to build an XGBoost model. You will also see how XGBoost works and why it is useful in machine learning.

We recommend going through the below article as well to fully understand the various terms and concepts mentioned in this article:

• A Comprehensive Guide to Ensemble Modeling (with Python codes)
• Ensemble Learning Course: Ensemble Learning and Ensemble Learning Techniques

What is XGBoost in Machine Learning?

XGBoost, or eXtreme Gradient Boosting, is a XGBoost algorithm in machine learning algorithm under ensemble learning. It is trendy for supervised learning tasks, such as regression and classification. XGBoost builds a predictive model by combining the predictions of multiple individual models, often decision trees, in an iterative manner.

The algorithm works by sequentially adding weak learners to the ensemble, with each new learner focusing on correcting the errors made by the existing ones. It uses a gradient descent optimization technique to minimize a predefined loss function during training.

Key features of XGBoost Algorithm include its ability to handle complex relationships in data, regularization techniques to prevent overfitting and incorporation of parallel processing for efficient computation.

Why Ensemble Learning?

XGBoost is an ensemble learning method. Sometimes, it may not be sufficient to rely upon the results of just one machine learning model. Ensemble learning offers a systematic solution to combine the predictive power of multiple learners. The resultant is a single model which gives the aggregated output from several models.

The models that form the ensemble, also known as base learners, could be either from the same learning algorithm or different learning algorithms. Bagging and boosting serve as two widely used ensemble learners. Though you can apply these techniques with several statistical models, decision trees dominate their usage.

Let’s briefly discuss bagging before taking a more detailed look at the concept of gradient boosting.

Bagging

While decision trees are one of the most easily interpretable models, they exhibit highly variable behavior. Consider a single training dataset that we randomly split into two parts. Now, let’s use each part to train a decision tree in order to obtain two models.

When we fit both these models, they would yield different results. Decision trees exhibit high variance due to this behavior. Bagging or boosting aggregation helps to reduce the variance in any learner. Several decision trees generated in parallel form the base learners of the bagging technique. Data sampled with replacement is fed to these learners for training. The final prediction is the averaged output from all the learners.

Boosting

In boosting, the trees build sequentially so that each subsequent tree aims to reduce the errors of the previous tree. Each tree learns from its predecessors and updates the residual errors. Hence, the tree that grows next in the sequence will learn from an updated version of the residuals.

The base learners in boosting are weak learners in which the bias is high, and the predictive power is just a tad better than random guessing. Each of these weak learners contributes some vital information for prediction, enabling the boosting technique to produce a strong learner by effectively combining these weak learners. The final strong learner brings down both the bias and the variance.

In contrast to bagging techniques like Random Forest, boosting uses trees with fewer splits. Such small trees, which are not very deep, are highly interpretable. You can optimally select parameters like the number of trees or iterations, the learning rate of gradient boosting, and the depth of the tree through validation techniques like k-fold cross-validation. Having a large number of trees might lead to overfitting. So, it is necessary to carefully choose the stopping criteria for boosting.

Gradient Boosting Ensemble Technique

The gradient boosting ensemble technique consists of three simple steps:

• An initial model F0 is defined to predict the target variable y. This model will be associated with a residual (y – F0)
• A new model h1 is fit to the residuals from the previous step
• Now, F0 and h1 are combined to give F1, the boosted version of F0. The mean squared error from F1 will be lower than that from F0:

To improve the performance of F1, we could model after the residuals of F1 and create a new model F2:

This can be done for ‘m’ iterations, until residuals have been minimized as much as possible:

Here, the additive learners do not disturb the functions created in the previous steps. Instead, they impart information of their own to bring down the errors.

Demonstrating the Potential of Gradient Boosting

In this section, we will explore the power of gradient boosting, a machine learning technique, by building an ensemble model to predict salary based on years of experience. By utilizing regression trees and optimizing loss functions, we aim to showcase the significant reduction in error that gradient boosting can achieve.

Introduction to the Predictive Model

Consider the following data where the years of experience is predictor variable and salary (in thousand dollars) is the target. Using regression trees as base learners, we can create an ensemble model to predict the salary. For the sake of simplicity, we can choose square loss as our loss function and our objective would be to minimize the square error.

Initializing the Model and Understanding Residuals

As the first step, the model should be initialized with a function F0(x). F0(x) should be a function which minimizes the loss function or MSE (mean squared error), in this case:

Taking the first differential of the above equation with respect to γ shows that the function minimizes at the mean i=1nyin. So, you can initiate the boosting model with:

F0(x) gives the predictions from the first stage of our model. Now, the residual error for each instance is (yi – F0(x)).

Building Additive Learners

We can use the residuals from F0(x) to create h1(x). h1(x) will be a regression tree which will try and reduce the residuals from the previous step. The output of h1(x) won’t be a prediction of y; instead, it will help in predicting the successive function F1(x) which will bring down the residuals.

The additive model h1(x) computes the mean of the residuals (y – F0) at each leaf of the tree. The boosted function F1(x) is obtained by summing F0(x) and h1(x). This way h1(x) learns from the residuals of F0(x) and suppresses it in F1(x).

This can be repeated for 2 more iterations to compute h2(x) and h3(x). Each of these additive learners, hm(x), will make use of the residuals from the preceding function, Fm-1(x).

The MSEs for F0(x), F1(x) and F2(x) are 875, 692 and 540. It’s amazing how these simple weak learners can bring about a huge reduction in error!

Observing the Reduction in Error

Note that each learner, hm(x), trains on the residuals. All the additive learners in boosting model themselves after the residual errors at each step. Intuitively, you can observe that the boosting learners use patterns in residual errors. At the stage where boosting reaches maximum accuracy, the residuals appear randomly distributed without any pattern.

Using Gradient d=Descent for Optimizing the Loss Function

In the case discussed above, MSE was the loss function. The mean minimized the error here. When MAE (mean absolute error) is the loss function, the median would be used as F₀(x) to initialize the model. A unit change in y would cause a unit change in MAE as well. Using scikit-learn, you can implement various models, including tree boosting algorithms and linear regression models to analyze the differences in loss functions and their impact on the model’s performance.

For MSE, the change observed would be roughly exponential. Instead of fitting hm(x) on the residuals, fitting it on the gradient of loss function, or the step along which loss occurs, would make this process generic and applicable across all loss functions.

Gradient descent helps us minimize any differentiable function. Earlier, the regression tree for hm(x) predicted the mean residual at each terminal node of the tree. In gradient boosting, the average gradient component would be computed.

For each node, there is a factor γ with which hm(x) is multiplied. This accounts for the difference in impact of each branch of the split. Gradient boosting helps in predicting the optimal gradient for the additive model, unlike classical gradient descent techniques which reduce error in the output at each iteration.

The following steps involve gradient boosting:

• F0(x) – with which we initialize the boosting algorithm – is to be defined:

• The gradient of the loss function is computed iteratively:

• Each hm(x) is fit on the gradient obtained at each step
• The multiplicative factor γm for each terminal node is derived and the boosted model Fm(x) is defined:

Unique Features of XGBoost Model

XGBoost model is a popular implementation of gradient boosting. Let’s discuss some features or metrics of XGBoost that make it so interesting:

• Regularization: XGBoost has an option to penalize complex models through both L1 and L2 regularization. Regularization helps in preventing overfitting
• Handling sparse data: Missing values or data processing steps like one-hot encoding make data sparse. XGBoost Classifier incorporates a sparsity-aware split finding algorithm to handle different types of sparsity patterns in the data
• Weighted quantile sketch: Most existing tree based algorithms can find the split points when the data points are of equal weights (using quantile sketch algorithm). However, they are not equipped to handle weighted data. XGBoost has a distributed weighted quantile sketch algorithm to effectively handle weighted data
• Block structure for parallel learning: For faster computing, XGBoost Classifier can make use of multiple cores on the CPU. This is possible because of a block structure in its system design. Data is sort and store data in in-memory units called blocks. Unlike other algorithms, this approach enables subsequent iterations to reuse the data layout instead of computing it again.
• This feature also serves useful for steps like split finding and column sub-sampling
• Cache awareness: In XGBoost machine learning, Scala requires non-continuous memory access to obtain the gradient statistics by row index. Hence, Tianqi Chen designed XGBoost to optimize hardware usage. This optimization occurs by allocating internal buffers in each thread, where the workflow can store the gradient statistics. And these parallel tree make better XGboost algorithms with the help of julia and java lanuages.
• Out-of-core computing: This feature optimizes the available disk space and maximizes its usage when handling huge datasets that do not fit into memory

Python Code for XGBoost

Here’s a live coding window to see how XGBoost works and play around with the code without leaving this article!

'''
The following code is for XGBoost
Created by - ANALYTICS VIDHYA
'''

# importing required libraries
import pandas as pd
from xgboost import XGBClassifier
from sklearn.metrics import accuracy_score

# read the train and test dataset
train_data = pd.read_csv('train-data.csv')
test_data = pd.read_csv('test-data.csv')

# shape of the dataset
print('Shape of training data :',train_data.shape)
print('Shape of testing data :',test_data.shape)

# Now, we need to predict the missing target variable in the test data
# target variable - Survived

# seperate the independent and target variable on training data
train_x = train_data.drop(columns=['Survived'],axis=1)
train_y = train_data['Survived']

# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=['Survived'],axis=1)
test_y = test_data['Survived']

'''
Create the object of the XGBoost model
You can also add other parameters and test your code here
Some parameters are : max_depth and n_estimators
Documentation of xgboost:

https://xgboost.readthedocs.io/en/latest/
'''
model = XGBClassifier()

# fit the model with the training data
model.fit(train_x,train_y)


# predict the target on the train dataset
predict_train = model.predict(train_x)
print('\nTarget on train data',predict_train) 

# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print('\naccuracy_score on train dataset : ', accuracy_train)

# predict the target on the test dataset
predict_test = model.predict(test_x)
print('\nTarget on test data',predict_test) 

# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print('\naccuracy_score on test dataset : ', accuracy_test)

XGBoost Model Benefits and Attributes

1. High accuracy: The XGBoost Classifier delivers high accuracy and consistently outperforms other machine learning algorithms in many predictive modeling tasks.
2. Scalability: It is highly scalable and can handle large datasets with millions of rows and columns.
3. Efficiency: The design ensures computational efficiency, allowing it to quickly train models on large datasets.
4. Flexibility: It supports a variety of data types and objectives, including regression, classification, and ranking problems.
5. Regularization: It incorporates regularization techniques to avoid overfitting and improve generalization performance.
6. Interpretability: It provides feature importance scores that can help users understand which features are most important for making predictions.
7. Open-source: XGBoost Model serves as an open-source library widely used and supported by the data science community.

XGBoost vs Gradient Boosting

Difference between XGBoost and Random Forest

Conclusion

So that was all about the mathematics that power the popular XGBoost algorithm. If your basics are solid, this article must have been a breeze for you. It’s such a powerful algorithm and while there are other techniques that have spawned from it (like CATBoost), XGBoost Model remains a game changer in the machine learning community. We highly recommend you to take up this course to sharpen your skills in machine learning and learn all the state-of-the-art techniques used in the field with our Applied Machine Learning – Beginner to Professional course. Also, these Algorithm helps you for training data. and help you for learning rate that will help you for lightgbm the algorithms.

Hope you like the article! The XGBoost algorithm is a strong tool in data science. The XGBoost classifier boosts accuracy by improving the model. Understanding how XGBoost works and how the XGBoost model functions is important. Here’s the XGBoost algorithm explained.

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