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.
Therefore, with the help of different ML algorithms, all of these weak learners are combined into one to form a strong learner.
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:
Boosting’s major objective is to focus more on misclassified predictions.
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:
Some of the features it offers are as follows:
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.