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XGBoost Classifier

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  • October 24, 2021

What is XGBoost classifier all about? What are its features? Why do people prefer it? We are going to discuss all this in detail.


Boosting is an ensemble learning method. It is basically a combination of algorithms that transforms weak learners into strong learners. Boosting algorithm is one of the most popular technique in data science competitions. It was declared by previous hackathons’ winners how they used this algorithm to improve their model’s accuracy.

One example of a weak learner is the classification of email as spam. Weak learners are unable of correctly classifying an email.

How can you enhance the way emails are classified as spam or not spam?

There are some indicators, for example, if an email has only a link, it is most likely spam, or if it contains only one picture that is an ad, it is almost certainly spam. Each of these factors are termed as weak learners. They are labelled as poor learners since we can’t truly ensure whether the output will be right or not if we rely on only one indicator and ignore the others. For example, if you asked a friend to email you a link to an Amazon product, our algorithm will automatically route this email to spam because it just contains a link. This is why we need more than one indicator or rule on which our algorithm can rely in order to classify an email as spam.

Boosting Algorithm in Ensemble Learning
Transformation of weak learners into a strong learner

Therefore, with the help of different ML algorithms, all of these weak learners are combined into one to form a strong learner.

How Does It Work?

The boosting process begins by identifying weak rules using ML techniques with different distributions. Every time you run the algorithm, it will generate a new weak rule. And we all know how this ends: all of these rules combine to form a powerful learner.

However, the key subject to boosting is selecting the correct distribution each and every time.


The following are the guidelines for selecting the appropriate distribution:

  • Firstly, the base learner accepts all distributions and gives each observation equal weight.
  • Secondly, there’s a potential that the first base learning technique caused a prediction failure. As a result, we should pay more attention to observations with a prediction error.
  • Finally, we apply the next algorithm and repeat the second phase until the base learning technique has strengthened and become more accurate.

Boosting’s major objective is to focus more on misclassified predictions.

XGBoost Classifier

Tianqi Chen’s gradient boosting machine implemented XGBoost to improve speed and performance. This algorithm has outperformed applied ML and Kaggle structured data competitions. It simply means Extreme Gradient Boosting. It’s basically a software library that you can download and install on your device with ease. The library is extremely flexible and portable. Therefore, it can be used in several computing contexts such as parallel tree building over multiple CPU cores, distributed computing for big models, out-of-core computing, and cache optimization to optimize hardware use and efficiency.

The XGBoost Classifier was designed and developed for the sole purpose of model performance and computational speed. It has shown to push the limits of processing power for boosted trees algorithms. It was designed to make the most of every bit of memory and hardware available to tree-boosting techniques. The XGBoost’s version includes a number of sophisticated features for model tweaking, computing environments, and algorithm improvement.

The XGBoost Classifier can conduct gradient boosting in three different ways:

  • Gradient Boosting
  • Stochastic Gradient Boosting
  • Regularized Gradient Boosting


Some of the features it offers are as follows:

  • Approximate Algorithm: You must first sort and organize the data before calculating the best split across a continuous feature. The approximation method is the method that enables this to be possible. It is a method that considers candidate split points and continuous features.
  • Column Block: This feature is for parallel learning. This feature basically stores the data in memory units known as ‘blocks’ in order to decrease sorting costs. Each block will include data columns that are sorted by the feature value that corresponds to it. This is a one-time computation which you should perform prior to training. You can reuse this computation several times.
  • Regularization:  It is necessary to establish an objective function to quantify the performance of a model given a set of parameters. Training loss and regularization make up the objective function. Regularization is a feature of the XGBoost Classifier that helps in limiting the model’s complexity and preventing overfitting.
  • Sparsity- aware: The XGBoost classifier is aware of the data’s sparsity pattern. Therefore, it only visits the non-missing elements in each node in the default direction. If you’re wondering why and how the data is sparse, it’s because of things like missing values or zero entries. When XGBoost detects a missing value at a node, it tries both the left and right hand splits and learns which route results in the most loss for each node.
  • Cross- Validation: XGBoost allows the user to do cross-validation at every iteration of the boosting process, making it simple to acquire the precise number of boosting iterations in a single run.

Gradient boosting techniques are powerful classifiers that usually perform well on structured data, and the XGBoost library is a fantastic implementation for it.

To begin with, it is an ensemble learning approach that is simple to apply. As previously mentioned, the library is extremely adaptable and portable. It’s incredibly fast, allowing you to perform several training cycles while fine-tuning the hyperparameters. The XGBoost classifier also includes a number of speed improvements that make it feasible to train a high-performing model in a short period of time. The XGboost classifier does not require large sample sizes for accurate prediction. It’s one of the few algorithms that can handle training samples of less than 100. Not only that but, it also helps to solve classification issues when the dataset is big (above 1000 rows) and contains missing values with both category and numerical features. You can also use the XGBoost algorithm on multiple problems without having to test numerous different methods.