presentation on xgboost

• XGBoost Tutorials
• Introduction to Boosted Trees
• Edit on GitHub

Introduction to Boosted Trees 

XGBoost stands for “Extreme Gradient Boosting”, where the term “Gradient Boosting” originates from the paper Greedy Function Approximation: A Gradient Boosting Machine , by Friedman.

The gradient boosted trees has been around for a while, and there are a lot of materials on the topic. This tutorial will explain boosted trees in a self-contained and principled way using the elements of supervised learning. We think this explanation is cleaner, more formal, and motivates the model formulation used in XGBoost.

Elements of Supervised Learning 

XGBoost is used for supervised learning problems, where we use the training data (with multiple features) \(x_i\) to predict a target variable \(y_i\) . Before we learn about trees specifically, let us start by reviewing the basic elements in supervised learning.

Model and Parameters 

The model in supervised learning usually refers to the mathematical structure of by which the prediction \(y_i\) is made from the input \(x_i\) . A common example is a linear model , where the prediction is given as \(\hat{y}_i = \sum_j \theta_j x_{ij}\) , a linear combination of weighted input features. The prediction value can have different interpretations, depending on the task, i.e., regression or classification. For example, it can be logistic transformed to get the probability of positive class in logistic regression, and it can also be used as a ranking score when we want to rank the outputs.

The parameters are the undetermined part that we need to learn from data. In linear regression problems, the parameters are the coefficients \(\theta\) . Usually we will use \(\theta\) to denote the parameters (there are many parameters in a model, our definition here is sloppy).

Objective Function: Training Loss + Regularization 

With judicious choices for \(y_i\) , we may express a variety of tasks, such as regression, classification, and ranking. The task of training the model amounts to finding the best parameters \(\theta\) that best fit the training data \(x_i\) and labels \(y_i\) . In order to train the model, we need to define the objective function to measure how well the model fit the training data.

A salient characteristic of objective functions is that they consist of two parts: training loss and regularization term :

where \(L\) is the training loss function, and \(\Omega\) is the regularization term. The training loss measures how predictive our model is with respect to the training data. A common choice of \(L\) is the mean squared error , which is given by

Another commonly used loss function is logistic loss, to be used for logistic regression:

The regularization term is what people usually forget to add. The regularization term controls the complexity of the model, which helps us to avoid overfitting. This sounds a bit abstract, so let us consider the following problem in the following picture. You are asked to fit visually a step function given the input data points on the upper left corner of the image. Which solution among the three do you think is the best fit?

The correct answer is marked in red. Please consider if this visually seems a reasonable fit to you. The general principle is we want both a simple and predictive model. The tradeoff between the two is also referred as bias-variance tradeoff in machine learning.

Why introduce the general principle? 

The elements introduced above form the basic elements of supervised learning, and they are natural building blocks of machine learning toolkits. For example, you should be able to describe the differences and commonalities between gradient boosted trees and random forests. Understanding the process in a formalized way also helps us to understand the objective that we are learning and the reason behind the heuristics such as pruning and smoothing.

Decision Tree Ensembles 

Now that we have introduced the elements of supervised learning, let us get started with real trees. To begin with, let us first learn about the model choice of XGBoost: decision tree ensembles . The tree ensemble model consists of a set of classification and regression trees (CART). Here’s a simple example of a CART that classifies whether someone will like a hypothetical computer game X.

We classify the members of a family into different leaves, and assign them the score on the corresponding leaf. A CART is a bit different from decision trees, in which the leaf only contains decision values. In CART, a real score is associated with each of the leaves, which gives us richer interpretations that go beyond classification. This also allows for a principled, unified approach to optimization, as we will see in a later part of this tutorial.

Usually, a single tree is not strong enough to be used in practice. What is actually used is the ensemble model, which sums the prediction of multiple trees together.

Here is an example of a tree ensemble of two trees. The prediction scores of each individual tree are summed up to get the final score. If you look at the example, an important fact is that the two trees try to complement each other. Mathematically, we can write our model in the form

where \(K\) is the number of trees, \(f_k\) is a function in the functional space \(\mathcal{F}\) , and \(\mathcal{F}\) is the set of all possible CARTs. The objective function to be optimized is given by

where \(\omega(f_k)\) is the complexity of the tree \(f_k\) , defined in detail later.

Now here comes a trick question: what is the model used in random forests? Tree ensembles! So random forests and boosted trees are really the same models; the difference arises from how we train them. This means that, if you write a predictive service for tree ensembles, you only need to write one and it should work for both random forests and gradient boosted trees. (See Treelite for an actual example.) One example of why elements of supervised learning rock.

Tree Boosting 

Now that we introduced the model, let us turn to training: How should we learn the trees? The answer is, as is always for all supervised learning models: define an objective function and optimize it !

Let the following be the objective function (remember it always needs to contain training loss and regularization):

Additive Training 

The first question we want to ask: what are the parameters of trees? You can find that what we need to learn are those functions \(f_i\) , each containing the structure of the tree and the leaf scores. Learning tree structure is much harder than traditional optimization problem where you can simply take the gradient. It is intractable to learn all the trees at once. Instead, we use an additive strategy: fix what we have learned, and add one new tree at a time. We write the prediction value at step \(t\) as \(\hat{y}_i^{(t)}\) . Then we have

It remains to ask: which tree do we want at each step? A natural thing is to add the one that optimizes our objective.

If we consider using mean squared error (MSE) as our loss function, the objective becomes

The form of MSE is friendly, with a first order term (usually called the residual) and a quadratic term. For other losses of interest (for example, logistic loss), it is not so easy to get such a nice form. So in the general case, we take the Taylor expansion of the loss function up to the second order :

where the \(g_i\) and \(h_i\) are defined as

After we remove all the constants, the specific objective at step \(t\) becomes

This becomes our optimization goal for the new tree. One important advantage of this definition is that the value of the objective function only depends on \(g_i\) and \(h_i\) . This is how XGBoost supports custom loss functions. We can optimize every loss function, including logistic regression and pairwise ranking, using exactly the same solver that takes \(g_i\) and \(h_i\) as input!

Model Complexity 

We have introduced the training step, but wait, there is one important thing, the regularization term ! We need to define the complexity of the tree \(\omega(f)\) . In order to do so, let us first refine the definition of the tree \(f(x)\) as

Here \(w\) is the vector of scores on leaves, \(q\) is a function assigning each data point to the corresponding leaf, and \(T\) is the number of leaves. In XGBoost, we define the complexity as

Of course, there is more than one way to define the complexity, but this one works well in practice. The regularization is one part most tree packages treat less carefully, or simply ignore. This was because the traditional treatment of tree learning only emphasized improving impurity, while the complexity control was left to heuristics. By defining it formally, we can get a better idea of what we are learning and obtain models that perform well in the wild.

The Structure Score 

Here is the magical part of the derivation. After re-formulating the tree model, we can write the objective value with the \(t\) -th tree as:

where \(I_j = \{i|q(x_i)=j\}\) is the set of indices of data points assigned to the \(j\) -th leaf. Notice that in the second line we have changed the index of the summation because all the data points on the same leaf get the same score. We could further compress the expression by defining \(G_j = \sum_{i\in I_j} g_i\) and \(H_j = \sum_{i\in I_j} h_i\) :

In this equation, \(w_j\) are independent with respect to each other, the form \(G_jw_j+\frac{1}{2}(H_j+\lambda)w_j^2\) is quadratic and the best \(w_j\) for a given structure \(q(x)\) and the best objective reduction we can get is:

The last equation measures how good a tree structure \(q(x)\) is.

If all this sounds a bit complicated, let’s take a look at the picture, and see how the scores can be calculated. Basically, for a given tree structure, we push the statistics \(g_i\) and \(h_i\) to the leaves they belong to, sum the statistics together, and use the formula to calculate how good the tree is. This score is like the impurity measure in a decision tree, except that it also takes the model complexity into account.

Learn the tree structure 

Now that we have a way to measure how good a tree is, ideally we would enumerate all possible trees and pick the best one. In practice this is intractable, so we will try to optimize one level of the tree at a time. Specifically we try to split a leaf into two leaves, and the score it gains is

This formula can be decomposed as 1) the score on the new left leaf 2) the score on the new right leaf 3) The score on the original leaf 4) regularization on the additional leaf. We can see an important fact here: if the gain is smaller than \(\gamma\) , we would do better not to add that branch. This is exactly the pruning techniques in tree based models! By using the principles of supervised learning, we can naturally come up with the reason these techniques work :)

For real valued data, we usually want to search for an optimal split. To efficiently do so, we place all the instances in sorted order, like the following picture.

A left to right scan is sufficient to calculate the structure score of all possible split solutions, and we can find the best split efficiently.

Limitation of additive tree learning

Since it is intractable to enumerate all possible tree structures, we add one split at a time. This approach works well most of the time, but there are some edge cases that fail due to this approach. For those edge cases, training results in a degenerate model because we consider only one feature dimension at a time. See Can Gradient Boosting Learn Simple Arithmetic? for an example.

Final words on XGBoost 

Now that you understand what boosted trees are, you may ask, where is the introduction for XGBoost? XGBoost is exactly a tool motivated by the formal principle introduced in this tutorial! More importantly, it is developed with both deep consideration in terms of systems optimization and principles in machine learning . The goal of this library is to push the extreme of the computation limits of machines to provide a scalable , portable and accurate library. Make sure you try it out, and most importantly, contribute your piece of wisdom (code, examples, tutorials) to the community!

XGBoost: A Scalable Tree Boosting System

XGBoost is an optimized distributed gradient boosting system designed to be highly efficient , flexible and portable . It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment(Hadoop, SGE, MPI) and can solve problems beyond billions of examples. The most recent version integrates naturally with DataFlow frameworks(e.g. Flink and Spark).

Reference Paper

• Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System . Preprint.

Technical Highlights

• Sparse aware tree learning to optimize for sparse data.
• Distributed weighted quantile sketch for quantile findings and approximate tree learning.
• Cache aware learning algorithm
• Out of core computation system for training when
• XGBoost is one of the most frequently used package to win machine learning challenges .
• XGBoost can solve billion scale problems with few resources and is widely adopted in industry.
• See XGBoost Resources Page for a complete list of usecases of XGBoost, including machine learning challenge winning solutions, data science tutorials and industry adoptions.

Acknowledgement

XGBoost open source project is actively developed by amazing contributors from DMLC/XGBoost community .

This work was supported in part by ONR (PECASE) N000141010672, NSF IIS 1258741 and the TerraSwarm Research Center sponsored by MARCO and DARPA.

• Tutorial on Tree Boosting [ Slides ]
• XGBoost Main Project Repo for python, R, java, scala and distributed version.
• XGBoost Julia Package
• XGBoost Resources for all resources including challenge winning solutions, tutorials.

XGBoost presentation

Tianqi chen, tong he, michaël benesty, 1.1 introduction, 1.2.1 github version, 1.2.2 cran version, 1.3.1 dataset presentation, 1.3.2 dataset loading, 1.3.3 basic training using xgboost, 1.4 basic prediction using xgboost, 1.5 perform the prediction, 1.6 transform the regression in a binary classification, 1.7 measuring model performance, 1.8.1 dataset preparation, 1.8.2 measure learning progress with xgb.train, 1.8.3 linear boosting, 1.8.4 manipulating xgb.dmatrix, 1.8.5 view feature importance/influence from the learnt model, 1 xgboost r tutorial.

XGBoost is short for e X treme G radient Boost ing package.

The purpose of this Vignette is to show you how to use XGBoost to build a model and make predictions.

It is an efficient and scalable implementation of gradient boosting framework by J. Friedman et al. (2000) and J. H. Friedman (2001) . Two solvers are included:

• linear model ;
• tree learning algorithm.

It supports various objective functions, including regression , classification and ranking . The package is made to be extendible, so that users are also allowed to define their own objective functions easily.

It has been used to win several Kaggle competitions.

It has several features:

• Speed: it can automatically do parallel computation on Windows and Linux , with OpenMP . It is generally over 10 times faster than the classical gbm .
• Dense Matrix: R ’s dense matrix, i.e.  matrix ;
• Sparse Matrix: R ’s sparse matrix, i.e.  Matrix::dgCMatrix ;
• Data File: local data files ;
• xgb.DMatrix : its own class (recommended).
• Sparsity: it accepts sparse input for both tree booster and linear booster , and is optimized for sparse input ;
• Customization: it supports customized objective functions and evaluation functions.

1.2 Installation

For weekly updated version (highly recommended), install from GitHub :

Windows user will need to install Rtools first.

The version 0.4-2 is on CRAN, and you can install it by:

Formerly available versions can be obtained from the CRAN archive

1.3 Learning

For the purpose of this tutorial we will load XGBoost package.

In this example, we are aiming to predict whether a mushroom can be eaten or not (like in many tutorials, example data are the same as you will use on in your every day life :-).

Mushroom data is cited from UCI Machine Learning Repository. Bache and Lichman (2013) .

We will load the agaricus datasets embedded with the package and will link them to variables.

The datasets are already split in:

• train : will be used to build the model ;
• test : will be used to assess the quality of our model.

Why split the dataset in two parts?

In the first part we will build our model. In the second part we will want to test it and assess its quality. Without dividing the dataset we would test the model on the data which the algorithm have already seen.

In the real world, it would be up to you to make this division between train and test data. The way to do it is out of the purpose of this article, however caret package may help .

Each variable is a list containing two things, label and data :

label is the outcome of our dataset meaning it is the binary classification we will try to predict.

Let’s discover the dimensionality of our datasets.

This dataset is very small to not make the R package too heavy, however XGBoost is built to manage huge dataset very efficiently.

As seen below, the data are stored in a dgCMatrix which is a sparse matrix and label vector is a numeric vector ( {0,1} ):

This step is the most critical part of the process for the quality of our model.

1.3.3.1 Basic training

We are using the train data. As explained above, both data and label are stored in a list .

In a sparse matrix, cells containing 0 are not stored in memory. Therefore, in a dataset mainly made of 0 , memory size is reduced. It is very usual to have such dataset.

We will train decision tree model using the following parameters:

• objective = "binary:logistic" : we will train a binary classification model ;
• max_depth = 2 : the trees won’t be deep, because our case is very simple ;
• nthread = 2 : the number of CPU threads we are going to use;
• nrounds = 2 : there will be two passes on the data, the second one will enhance the model by further reducing the difference between ground truth and prediction.

More complex the relationship between your features and your label is, more passes you need.

1.3.3.2 Parameter variations

1.3.3.2.1 dense matrix.

Alternatively, you can put your dataset in a dense matrix, i.e. a basic R matrix.

1.3.3.2.2 xgb.DMatrix

XGBoost offers a way to group them in a xgb.DMatrix . You can even add other meta data in it. It will be useful for the most advanced features we will discover later.

1.3.3.2.3 Verbose option

XGBoost has several features to help you to view how the learning progress internally. The purpose is to help you to set the best parameters, which is the key of your model quality.

One of the simplest way to see the training progress is to set the verbose option (see below for more advanced techniques).

The purpose of the model we have built is to classify new data. As explained before, we will use the test dataset for this step.

These numbers doesn’t look like binary classification {0,1} . We need to perform a simple transformation before being able to use these results.

The only thing that XGBoost does is a regression . XGBoost is using label vector to build its regression model.

How can we use a regression model to perform a binary classification?

If we think about the meaning of a regression applied to our data, the numbers we get are probabilities that a datum will be classified as 1 . Therefore, we will set the rule that if this probability for a specific datum is > 0.5 then the observation is classified as 1 (or 0 otherwise).

To measure the model performance, we will compute a simple metric, the average error .

Note that the algorithm has not seen the test data during the model construction.

Steps explanation:

• as.numeric(pred > 0.5) applies our rule that when the probability (<=> regression <=> prediction) is > 0.5 the observation is classified as 1 and 0 otherwise ;
• probabilityVectorPreviouslyComputed != test$label computes the vector of error between true data and computed probabilities ;
• mean(vectorOfErrors) computes the average error itself.

The most important thing to remember is that to do a classification, you just do a regression to the label and then apply a threshold .

Multiclass classification works in a similar way.

This metric is 0.02 and is pretty low: our yummy mushroom model works well!

1.8 Advanced features

Most of the features below have been implemented to help you to improve your model by offering a better understanding of its content.

For the following advanced features, we need to put data in xgb.DMatrix as explained above.

Both xgboost (simple) and xgb.train (advanced) functions train models.

One of the special feature of xgb.train is the capacity to follow the progress of the learning after each round. Because of the way boosting works, there is a time when having too many rounds lead to an overfitting. You can see this feature as a cousin of cross-validation method. The following techniques will help you to avoid overfitting or optimizing the learning time in stopping it as soon as possible.

One way to measure progress in learning of a model is to provide to XGBoost a second dataset already classified. Therefore it can learn on the first dataset and test its model on the second one. Some metrics are measured after each round during the learning.

in some way it is similar to what we have done above with the average error. The main difference is that below it was after building the model, and now it is during the construction that we measure errors.

For the purpose of this example, we use watchlist parameter. It is a list of xgb.DMatrix , each of them tagged with a name.

XGBoost has computed at each round the same average error metric than seen above (we set nrounds to 2, that is why we have two lines). Obviously, the train-error number is related to the training dataset (the one the algorithm learns from) and the test-error number to the test dataset.

Both training and test error related metrics are very similar, and in some way, it makes sense: what we have learned from the training dataset matches the observations from the test dataset.

If with your own dataset you have not such results, you should think about how you divided your dataset in training and test. May be there is something to fix. Again, caret package may help .

For a better understanding of the learning progression, you may want to have some specific metric or even use multiple evaluation metrics.

eval_metric allows us to monitor two new metrics for each round, logloss and error .

Until now, all the learnings we have performed were based on boosting trees. XGBoost implements a second algorithm, based on linear boosting. The only difference with previous command is booster = "gblinear" parameter (and removing eta parameter).

In this specific case, linear boosting gets slightly better performance metrics than decision trees based algorithm.

In simple cases, it will happen because there is nothing better than a linear algorithm to catch a linear link. However, decision trees are much better to catch a non linear link between predictors and outcome. Because there is no silver bullet, we advise you to check both algorithms with your own datasets to have an idea of what to use.

1.8.4.1 Save / Load

Like saving models, xgb.DMatrix object (which groups both dataset and outcome) can also be saved using xgb.DMatrix.save function.

1.8.4.2 Information extraction

Information can be extracted from xgb.DMatrix using getinfo function. Hereafter we will extract label data.

Feature importance is similar to R gbm package’s relative influence (rel.inf).

1.8.5.1 View the trees from a model

You can dump the tree you learned using xgb.dump into a text file.

You can plot the trees from your model using ` xgb.plot.tree

if you provide a path to fname parameter you can save the trees to your hard drive.

1.8.5.2 Save and load models

Maybe your dataset is big, and it takes time to train a model on it? May be you are not a big fan of losing time in redoing the same task again and again? In these very rare cases, you will want to save your model and load it when required.

Hopefully for you, XGBoost implements such functions.

xgb.save function should return TRUE if everything goes well and crashes otherwise.

An interesting test to see how identical our saved model is to the original one would be to compare the two predictions.

result is 0 ? We are good!

In some very specific cases, like when you want to pilot XGBoost from caret package, you will want to save the model as a R binary vector. See below how to do it.

Again 0 ? It seems that XGBoost works pretty well!

Free experiment tracking for academic researchers, professors, students, and Kagglers -> Learn more 💡

XGBoost: Everything You Need to Know

XGBoost is a popular gradient-boosting framework that supports GPU training, distributed computing, and parallelization. It’s precise, it adapts well to all types of data and supervised learning problems, it has excellent documentation , and overall, it’s very easy to use.

Over many years, it has become the de facto standard algorithm for getting accurate results from predictive modeling with machine learning. It’s one of the fastest gradient-boosting libraries for R, Python, and C++..

We are going to explore how to use XGBoost’s models and how to track their performance using Neptune. If you’re not sure that XGBoost is a great choice for you, follow along with the tutorial until the end, and then you’ll be able to make a fully informed decision.

What are ensemble algorithms?

Ensemble learning combines several learners (models) to improve overall performance, increasing predictiveness and accuracy in machine learning and predictive modeling.

Technically speaking, the power of ensemble models is simple: they can combine thousands of smaller learners trained on subsets of the original data. This can lead to interesting observations, like:

• The variance of the general model decreases significantly thanks to bagging .
• The bias also decreases due to boosting .
• And overall predictive power improves because of stacking.  

Types of ensemble methods

Ensemble methods can be classified into two groups based on how the sub-learners are generated:

• Sequential ensemble methods: learners are generated sequentially. These methods use the dependency between base learners. A popular example of sequential ensemble algorithms is AdaBoost . 
• Parallel ensemble methods: learners are generated in parallel. The base learners are created independently to study and exploit the effects related to their independence and reduce error by averaging the results. An example of implementing this approach is Random Forests .

Ensemble methods can use homogeneous learners (learners from the same family) or heterogeneous learners (learners from multiple sorts, as accurate and diverse as possible).

Homogenous and heterogenous ML algorithms  

Homogeneous ensemble methods typically use a single type of base learning algorithm, diversifying the training data by weighting samples. 

Heterogeneous ensembles, on the other hand, consist of members with different base learning algorithms that can be combined and used simultaneously to form the predictive model. 

A general rule of thumb: 

• Heterogeneous ensembles use different feature selection methods with the same data.
• Homogeneous ensembles use the same feature selection with a variety of data and distribute the dataset over several nodes.

Homogeneous Ensembles:

• Ensemble algorithms that use bagging, like Decision Tree Classifiers
• Random Forests , Randomized Decision Trees

Heterogeneous Ensembles:

• Support Vector Machines , SVM
• Artificial Neural Networks , ANN
• Memory-Based Learning methods
• Bagged and Boosted decision Trees like XGBoost

Important characteristics of ensemble algorithms

Bagging, short for Bootstrap Aggregating, is a technique that reduces overall variance by combining multiple models. It works by creating multiple subsets of the original dataset through random sampling with replacement, a process known as bootstrapping. A separate model is then trained on each of these subsets. When making predictions, bagging combines the outputs of all these models. For classification problems, such as in Random Forests, it typically uses majority voting to determine the final prediction. For regression problems, it usually averages the predictions of all models. This approach improves overall performance by mitigating the impact of individual model errors, effectively decreasing the variance of the final prediction.

Note: Remember that some learners are stable and less sensitive to training perturbations. Such learners, when combined, don’t help the general model improve generalization performance.

This technique matches weak learners—learners that have poor predictive power and do slightly better than random guessing—to a specific weighted subset of the original dataset. Higher weights are given to subsets that were misclassified earlier.

Learner predictions are then combined with voting mechanisms in the case of classification or a weighted sum for regression.

Well-known boosting algorithms 

AdaBoost stands for Adaptive Boosting. The logic implemented in the algorithm is: 

• First-round classifiers (learners) are all trained using equally weighted coefficients.
• In subsequent boosting rounds, the adaptive process increasingly weighs data points that were misclassified by the learners in previous rounds and decreases the weights for correctly classified ones. 

If you’re curious about the algorithm’s description, take a look at the image below:

Gradient Boosting

Gradient Boosting uses differentiable function losses from weak learners to generalize. At each boosting stage, the learners are used to minimize the loss function given the current model. Boosting algorithms can be used either for classification or regression. 

What is XGBoost architecture?

XGBoost stands for eXtreme Gradient Boosting. It’s a parallelized and carefully optimized version of the gradient boosting algorithm. Parallelizing the whole boosting process improves the training time significantly. 

Instead of training the best possible model on the data (like in traditional methods), we train thousands of models on various subsets of the training dataset and then vote for the best-performing model.

In many cases, XGBoost is better than usual gradient-boosting algorithms. The Python implementation gives access to a vast number of inner parameters to tweak for better precision and accuracy.

Some important features of XGBoost are:

• Parallelization: The model is implemented to train with multiple CPU cores.
• Regularization: XGBoost includes different regularization penalties to avoid overfitting. Penalty regularizations produce successful training, so the model can generalize adequately.
• Non-linearity: XGBoost can detect and learn from non-linear data patterns.
• Cross-validation: built-in and comes out-of-the-box.
• Scalability: XGBoost can run distributedly thanks to distributed servers and clusters like Hadoop and Spark, so you can process enormous amounts of data. It’s also available in many programming languages like C++, JAVA, Python, and Julia. 

For more details and an in-depth look at the mathematics behind gradient boosting, I recommend you check out this post by Krishna Kumar Mahto .

Other Gradient Boosting methods

Gradient boosting machine (gbm).

GBM combines predictions from multiple decision trees, and all the weak learners are decision trees. The key idea with this algorithm is that every node of those trees takes a different subset of features to select the best split. As it’s a Boosting algorithm, each new tree learns from the errors made in the previous ones.

Useful reference  -> Understanding Gradient Boosting Machines

Light Gradient Boosting Machine (LightGBM)

LightGBM can handle huge amounts of data. It’s one of the fastest algorithms for both training and prediction. It generalizes well, meaning that it can be used to solve similar problems. It scales well to large numbers of cores and has open-source code, so you can use it in your projects for free.

Categorical Boosting (CatBoost)

The CatBoost algorithm , a specific form of gradient boosting, specializes in working with very diverse kinds of data. It shines with categorical data, but it also does well with numeric data and with datasets that contain both kinds of variables. Most gradient-boosting algorithms can work reasonably well with categorical variables, but CatBoost outperforms them because of how it handles these variables. Not only is CatBoost good at learning with the kinds of variables that many machine learning models struggle with, but it also learns efficiently from unlabeled data—that is, from data without any obvious outcomes or targets.

Integrating XGBoost with neptune.ai

Even though XGBoost is very powerful without hyperparameter tuning, you are likely to spend much of your time experimenting with different model architectures and feature engineering techniques for your given problem. After a few experiments, keeping track of the results of each run becomes nearly impossible due to the vast amount of metadata you have to record.

For this reason, XGBoost’s best friend is an experiment-tracking library like Neptune. Neptune offers an XGBoost integration that can automatically capture all the training details and any additional metadata and visualize them nicely in a dashboard. 

We will see how to use XGBoost in combination with Neptune on a regression problem using a sample dataset.

ML Experiment Tracking: Why It Matters, How to Implement it

Setting up the environment.

After signing up for an account , create your first Neptune project to store XGBoost models and their related metadata. Then, open up your terminal and create a virtual environment:

Then, install the libraries we will need:

Here is what the libraries do:

• neptune.ai : Experiment tracking platform
• neptune-xgboost : Neptune integration for XGBoost
• xgboost : Gradient boosting library
• scikit-learn : Machine learning tools and utilities
• pandas : Data manipulation and analysis
• seaborn : Statistical data visualization
• ipykernel : IPython kernel for Jupyter
• python-dotenv : Reading environment variables in Python

These libraries provide a comprehensive set of tools for data processing, model training, evaluation, visualization, and experiment tracking in a machine-learning workflow specifically tailored for XGBoost with Neptune integration.

If you are going to use Jupyter notebooks instead of Python scripts, run the following command to add the newly-created Conda environment as a Jupyter kernel:

Next, create a file named .env in your current working directory:

Paste the following contents into the file:

Now, you are ready to write some code!

Loading a sample dataset

We will use the Diamonds dataset , which is built into Seaborn:

As you can see, the dataset contains categorical features that need to be encoded. We will use the get_dummies() function of Pandas to take care of them:

After encoding, we extract the target column, the price of diamonds, into the y variable and split the data. Next, we will build DMatrix objects, which are specialized classes to represent datasets in the most efficient way for XGBoost:

We will pass these objects to XGBoost during training. 

Creating a Neptune run object

The next step is connecting your notebook or script to Neptune. The way we will do this is by creating a Neptune run object:

First, we import the os and load_dotenv modules to read the environment variables stored in your .env file. Then, we create the project_name and api_token variables using the .getenv() function. 

This allows us to create a run object with the init_run() function, establishing a connection between our environment and Neptune. 

We can already log some metadata using the run object. Let’s add model parameters:

Once the above line executes, you can visit your project dashboard and see the parameters logged there. 

Training a model in XGBoost and logging metadata to neptune.ai

Now, we are ready to train our first XGBoost model:

We start by importing the NeptuneCallback class and then define the number of boosting rounds. Following that, we initialize the callback class by passing the run object. 

Then, the training is as simple as calling the train() function of xgb with the following parameters:

• params : The dictionary of XGBoost parameters we defined earlier
• dtrain : The training data in DMatrix format
• num_rounds : The number of boosting rounds (iterations)
• evals : A list of tuples, each containing a dataset and its name for evaluation during training
• callbacks : A list of callback functions, including our neptune_callback

The evals parameter allows us to monitor both training and test performance during the training process. The callbacks parameter, with our neptune_callback , ensures that Neptune logs metrics and other relevant information at each iteration, providing real-time insights into the model’s training progress.

Once the above snippet finishes execution, call:

indicating that our first experiment has ended. You should receive an output like below:

with a link to the experiment’s run page on your dashboard.

Inspecting and analyzing the experiment results

Clicking on the link and switching to the charts pane brings you to plots like below:

If you switch to the Images tab , you should see a feature importance plot as well:

The XGBRegressor generally classifies the order of importance of each feature used for the prediction. A benefit of using gradient boosting is that, after the boosted trees are constructed, it is relatively straightforward to retrieve importance scores for each attribute. This can be done by computing a feature importance graph and visualizing the similarity between each feature (feature-wise or attribute-wise) within the boosted trees. 

We can see that diamond carat and depth are the principal factors in a diamond’s price. 

Overview of some XGBoost hyperparameters

Let’s take a closer look and try to explain the XGBRegressor hyperparameters:

• learning_rate : Used to control and adjust the weighting of the internal model estimators. The learning_rate should always be a small value to force long-term learning. 
• max_depth : Indicates the depth degree of the estimators (trees in this case). Manipulate this parameter carefully, because it will cause the model to overfit. 
• alpha : A specific type of penalty regularization (L1) to help avoid overfitting
• num_estimators : The number of estimators the model will be built upon.
• num_boost_round : The number of boosting stages. Although num_estimators and num_boost_round remain quite the same, you should keep in mind that the num_boost_round should be re-tuned each time you update a parameter.  

Versioning your model

You can store multiple versions of your model in binary format in Neptune. Neptune automatically saves the current version of the model once the training is finished, so you can always get back and load previous model versions to compare.

Under the Training section , you’ll find all relevant metadata that’s been stored:

Collaborate and share work with your team

The Neptune platform also lets you cross-compare all your experiments in a seamless manner. Simply check the experiments you want, and a specific view will appear that shows all the required information.

You can share any Neptune experiment by just sending a link .

Note: Using the team plan, you can share all your work and projects with your teammates. 

How to hyper-tune the XGBRegressor

The most efficient way of dealing with parameter tuning when time and resources are not an issue is to run a gigantic Grid Search on all the parameters and wait for the algorithm to output the optimal solution. It’s good to do so if you’re exploiting a small or intermediate dataset. But for bigger datasets, this approach can very quickly turn into a nightmare and consume too much time and too many resources.

Tips for hyper-tuning XGBoost when dealing with huge datasets

A well-known saying among data scientists goes like this: “You can make your model do wonders if your data has some signal, and if it doesn’t have a signal, it doesn’t.”.

The most straightforward approach I suggest when having vast amounts of training data is to try to manually research the features that have a significant predictive impact. 

• Firstly, try to reduce your features. 100 or 200 features is a lot; you should try to narrow the scope of feature selection. You could also rely on SelectKBest to select the top performers according to a specific criterion, in which each feature scores a K number of points and is chosen accordingly. 
• Bad performance can also be related to the quality assessment of your testing dataset. The test data might represent a completely different subset of data than your training data.  Therefore, you should try doing cross-validation so that the R-squared value on the features is confident enough and sufficiently reliable.
• Finally, if you see that your hyperparameter tuning still has minimal impact, try to switch to simpler regression methods like linear regression, Lasso, and Elastic Net, instead of sticking to XGBRegression.  

Since the data for our example isn’t that big, we can choose to go for the first option. However, since the goal here is to expose the more reliable option for model tuning that you can leverage, we’ll go for this option without hesitation. Keep in mind that if you know which hyperparameters have more impact on the data, you’ll have a much smaller scope of work.

Grid search 

Fortunately, XGBoost implements the scikit-learn API , so it’s very easy to use Grid Search and start rolling out the optimal results for the model based on the set of original hyperparameters.

Let’s create a range of values that each hyperparameter can take:

We also create an XGBRegressor object using the Scikit-learn API of XGBoost, which is a requirement.

Now, we import one new module and a function:

Then, we reprepare our data:

And pass it to the GridSearchCV class:

The class will find the best set of parameters from our defined range based on the RMSE score of each parameter combination. Once the execution finishes, you can call best_params_ attribute of the grid_search object to see the best found parameters:

XGBoost pros and cons

• Gradient Boosting comes with an easy-to-read and interpretable algorithm, making most of its predictions easy to handle.
• Boosting is a resilient and robust method that prevents and curbs over-fitting quite easily.
• XGBoost performs very well on medium, small, and structured datasets with not too many features. 
• It is a great approach because the majority of real-world problems involve classification and regression, two tasks where XGBoost is the reigning king. 

Disadvantages 

• XGBoost does not perform so well on sparse and unstructured data.
• A common thing often forgotten is that Gradient Boosting is very sensitive to outliers since every classifier is forced to fix the errors in the predecessor learners. 
• The overall method is hardly scalable. This is because the estimators base their correctness on previous predictors, hence the procedure involves a lot of struggle to streamline. 

We’ve covered many aspects of Gradient Boosting, starting from a theoretical point of view to a more practical path. Now you can see how easy it is to add experiment tracking and model management to your XGBoost training and hyper-tuning with Neptune.

As always, I’ll leave you with some useful references below, so you can expand your pool of knowledge even more and improve your coding skills. 

Stay tuned for more content!

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Table of Contents

What is xgboost algorithm, what is xgboost in machine learning, xgboost features, xgboost formula, why xgboost, what algorithm does xgboost use, xgboost benefits and attributes, choose the right program, what is xgboost an introduction to xgboost algorithm in machine learning.

Since its introduction in 2014, XGBoost has become the machine learning algorithm of choice for data scientists and machine learning engineers . It's an open-source library that can train and test models on large amounts of data. It has been used in many domains, from predicting ad click-through rates to classifying high-energy physics events.

XGBoost is particularly popular because it's so fast, and that speed comes at no cost to accuracy!

XGBoost is a robust machine-learning algorithm that can help you understand your data and make better decisions.

XGBoost is an implementation of gradient-boosting decision trees. It has been used by data scientists and researchers worldwide to optimize their machine-learning models.

XGBoost is designed for speed, ease of use, and performance on large datasets. It does not require optimization of the parameters or tuning, which means that it can be used immediately after installation without any further configuration.

XGBoost is a widespread implementation of gradient boosting. Let’s discuss some features of XGBoost that make it so attractive.

• XGBoost offers regularization, which allows you to control overfitting by introducing L1/L2 penalties on the weights and biases of each tree. This feature is not available in many other implementations of gradient boosting.
• Another feature of XGBoost is its ability to handle sparse data sets using the weighted quantile sketch algorithm. This algorithm allows us to deal with non-zero entries in the feature matrix while retaining the same computational complexity as other algorithms like stochastic gradient descent.
• XGBoost also has a block structure for parallel learning. It makes it easy to scale up on multicore machines or clusters. It also uses cache awareness, which helps reduce memory usage when training models with large datasets.
• Finally, XGBoost offers out-of-core computing capabilities using disk-based data structures instead of in-memory ones during the computation phase.

XgBoost is a gradient boosting algorithm for supervised learning. It's a highly efficient and scalable implementation of the boosting algorithm, with performance comparable to that of other state-of-the-art machine learning algorithms in most cases.

Following is the XGBoost formula:

XGBoost is used for these two reasons: execution speed and model performance.

Execution speed is crucial because it's essential to working with large datasets. When you use XGBoost, there are no restrictions on the size of your dataset, so you can work with datasets that are larger than what would be possible with other algorithms.

Model performance is also essential because it allows you to create models that can perform better than other models. XGBoost has been compared to different algorithms such as random forest (RF), gradient boosting machines (GBM), and gradient boosting decision trees (GBDT). These comparisons show that XGBoost outperforms these other algorithms in execution speed and model performance.

Gradient boosting is a ML algorithm that creates a series of models and combines them to create an overall model that is more accurate than any individual model in the sequence.

It supports both regression and classification predictive modeling problems.

To add new models to an existing one, it uses a gradient descent algorithm called gradient boosting.

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XGBoost is a highly portable library on OS X, Windows, and Linux platforms. It's also used in production by organizations across various verticals, including finance and retail.

XGBoost is open source, so it's free to use, and it has a large and growing community of data scientists actively contributing to its development. The library was built from the ground up to be efficient, flexible, and portable.

You can use XGBoost for classification, regression, ranking, and even user-defined prediction challenges! You can also use this library with other tools like H2O or Scikit-Learn if you want to get more out of your model-building process.

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1. What is the use of XGBoost?

The main reasons why you should consider using XGBoost are:

• It is more efficient than other machine-learning algorithms
• It allows you to handle large datasets easily

2. What is XGBoost, and how does it work?

XGBoost is a powerful open-source tool for machine learning. It's designed to help you build better models and works by combining decision trees and gradient boosting.

3. Is XGBoost a classification or regression?

XGBoost is a classification algorithm. It's designed for problems where you have a bunch of training data that can be used to create a classifier, and then you have new data that you want to classify.

4. Is XGBoost boosting algorithm?

XGBoost is a boosting algorithm.

It takes in training data, uses it to train a model, and then evaluates the model on new data. This process repeats until the model stops improving.

5. How do you explain XGBoost in an interview?

XGBoost is a robust algorithm that can help you improve your machine-learning model's accuracy. It's based on gradient boosting and can be used to fit any decision tree-based model.

The way it works is simple: you train the model with values for the features you have, then choose a hyperparameter (like the number of trees) and optimize it so that your model has the highest possible accuracy.

6. How is XGBoost different from Random Forest?

XGBoost is a boosting algorithm that uses bagging, which trains multiple decision trees and then combines the results. It allows XGBoost to learn more quickly than other algorithms but also gives it an advantage in situations with many features to consider.

Random Forest is a classification algorithm that uses decision trees as its base learning model. The underlying assumption of Random Forest is that each tree will make different mistakes, so combining the results of multiple trees should be more accurate than any single tree.

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Title: xgboost: a scalable tree boosting system.

Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

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XGBoost is an open-source software library that implements optimized distributed gradient boosting machine learning algorithms under the Gradient Boosting framework.

What is XGBoost?

XGBoost , which stands for Extreme Gradient Boosting, is a scalable, distributed gradient-boosted decision tree (GBDT) machine learning library. It provides parallel tree boosting and is the leading machine learning library for regression, classification, and ranking problems.

It’s vital to an understanding of XGBoost to first grasp the machine learning concepts and algorithms that XGBoost builds upon: supervised machine learning, decision trees, ensemble learning, and gradient boosting .

Supervised machine learning uses algorithms to train a model to find patterns in a dataset with labels and features and then uses the trained model to predict the labels on a new dataset’s features.

Decision trees create a model that predicts the label by evaluating a tree of if-then-else true/false feature questions, and estimating the minimum number of questions needed to assess the probability of making a correct decision. Decision trees can be used for classification to predict a category, or regression to predict a continuous numeric value. In the simple example below, a decision tree is used to estimate a house price (the label) based on the size and number of bedrooms (the features).

A Gradient Boosting Decision Trees (GBDT) is a decision tree ensemble learning algorithm similar to random forest, for classification and regression. Ensemble learning algorithms combine multiple machine learning algorithms to obtain a better model.

Both random forest and GBDT build a model consisting of multiple decision trees. The difference is in how the trees are built and combined.

Random forest uses a technique called bagging to build full decision trees in parallel from random bootstrap samples of the data set. The final prediction is an average of all of the decision tree predictions.

The term “gradient boosting” comes from the idea of “boosting” or improving a single weak model by combining it with a number of other weak models in order to generate a collectively strong model. Gradient boosting is an extension of boosting where the process of additively generating weak models is formalized as a gradient descent algorithm over an objective function. Gradient boosting sets targeted outcomes for the next model in an effort to minimize errors. Targeted outcomes for each case are based on the gradient of the error (hence the name gradient boosting) with respect to the prediction.

GBDTs iteratively train an ensemble of shallow decision trees, with each iteration using the error residuals of the previous model to fit the next model. The final prediction is a weighted sum of all of the tree predictions. Random forest “bagging” minimizes the variance and overfitting, while GBDT “boosting” minimizes the bias and underfitting.

XGBoost is a scalable and highly accurate implementation of gradient boosting that pushes the limits of computing power for boosted tree algorithms, being built largely for energizing machine learning model performance and computational speed. With XGBoost, trees are built in parallel, instead of sequentially like GBDT. It follows a level-wise strategy, scanning across gradient values and using these partial sums to evaluate the quality of splits at every possible split in the training set. 

Why XGBoost?

XGBoost gained significant favor in the last few years as a result of helping individuals and teams win virtually every Kaggle structured data competition. In these competitions, companies and researchers post data after which statisticians and data miners compete to produce the best models for predicting and describing the data.

Initially both Python and R implementations of XGBoost were built. Owing to its popularity, today XGBoost has package implementations for Java , Scala , Julia , Perl , and other languages. These implementations have opened the XGBoost library to even more developers and improved its appeal throughout the Kaggle community.

XGBoost has been integrated with a wide variety of other tools and packages such as scikit-learn for Python enthusiasts and caret for R users. In addition, XGBoost is integrated with distributed processing frameworks like Apache Spark and Dask.

In 2019 XGBoost was named among InfoWorld’s coveted Technology of the Year award winners.

XGBoost Benefits and Attributes

The list of benefits and attributes of XGBoost is extensive, and includes the following:

• A large and growing list of data scientists globally that are actively contributing to XGBoost open source development
• Usage on a wide range of applications, including solving problems in regression, classification, ranking, and user-defined prediction challenges
• A library that’s highly portable and currently runs on OS X, Windows, and Linux platforms
• Cloud integration that supports AWS, Azure, Yarn clusters , and other ecosystems
• Active production use in multiple organizations across various vertical market areas
• A library that was built from the ground up to be efficient, flexible, and portable

XGBoost and Data Scientists

It’s noteworthy for data scientists that XGBoost and XGBoost machine learning models have the premier combination of prediction performance and processing time compared with other algorithms. This has been borne out by various benchmarking studies and further explains its appeal to data scientists.

How XGBoost Runs Better with GPUs

CPU-powered machine learning tasks with XGBoost can literally take hours to run. That’s because creating highly accurate, state-of-the-art prediction results involves the creation of thousands of decision trees and the testing of large numbers of parameter combinations. Graphics processing units, or GPUs, with their massively parallel architecture consisting of thousands of small efficient cores, can launch thousands of parallel threads simultaneously to supercharge compute-intensive tasks.

NVIDIA developed NVIDIA RAPIDS ™ —an open-source data analytics and machine learning acceleration platform—or executing end-to-end data science training pipelines completely in GPUs. It relies on NVIDIA CUDA ® primitives for low-level compute optimization, but exposes that GPU parallelism and high memory bandwidth through user-friendly Python interfaces. 

Focusing on common data preparation tasks for analytics and data science, RAPIDS offers a familiar DataFrame API that integrates with scikit-learn and a variety of machine learning algorithms without paying typical serialization costs. This allows acceleration for end-to-end pipelines—from data prep to machine learning to deep learning. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.

XGBoost + RAPIDS

The RAPIDS team works closely with the Distributed Machine Learning Common (DMLC) XGBoost organization, and XGBoost now includes seamless, drop-in GPU acceleration. This significantly speeds up model training and improves accuracy for better predictions.

XGBoost now builds on the GoAI interface standards to provide zero-copy data import from cuDF, cuPY, Numba, PyTorch, and others. The Dask API makes it easy to scale to multiple nodes or multiple GPUs, and the  RAPIDS Memory Manager (RMM) integrates with XGBoost, so you can share a single, high-speed memory pool.

GPU-Accelerated XGBoost

The GPU-accelerated XGBoost algorithm makes use of fast parallel prefix sum operations to scan through all possible splits, as well as parallel radix sorting to repartition data. It builds a decision tree for a given boosting iteration, one level at a time, processing the entire dataset concurrently on the GPU. 

GPU-Accelerated, End-to-End Data Pipelines with Spark + XGBoost

NVIDIA understands that machine learning at scale delivers powerful predictive capabilities for data scientists and developers and, ultimately, to end users. But this at-scale learning depends upon overcoming key challenges to both on-premises and cloud infrastructure, like speeding up pre-processing of massive data volumes and then accelerating compute-intensive model training.

NVIDIA’s initial release of spark-xgboost enabled training and inferencing of XGBoost machine learning models across Apache Spark nodes. This has helped make it a leading mechanism for enterprise-class distributed machine learning.

GPU-Accelerated Spark XGBoost speeds up pre-processing of massive volumes of data, allows larger data sizes in GPU memory, and improves XGBoost training and tuning time.

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