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Introduction
Machine learning has gained a lot of importance in recent years because of its revolutionary applications that help in solving a plethora of problems. The machine learning techniques are such that the results make everybody awestruck. One such technique is boosting which has brought a significant difference in these results and has improved the predictions. We will talk about a boosting machine learning algorithm i.e., XGBoost.
Boosting is an sequential ensemble machine learning technique that encompasses a family of machine learning algorithms that combine weak learners sequentially while training to strong learners to improve the performance of the model and get correct predictions.
Working of Boosting Algorithms?
The principle on which boosting algorithms work upon is combination of multiple weak learners during the training phase to produce strong learners. These weak learners are generated in multiple iterations by creating multiple decision stumps which later on give great results. The major focus is on incorrect predictions that the algorithm makes that are given higher weightage in the next iteration to make correct predictions as much as possible.
There are 3 common boosting algorithms typically used that are AdaBoost, Gradient Descent Boosting, XGBoost. This blog will give an overview of what is XGBoost.
What is XGBoost
XGBoost is also known as eXtreme Gradient boosting, and is an extended version of the gradient boosting method. It was first developed as a research project by Tianqi Chen and Carlos Guestring, from University of Washington XGBoost is an ensemble distributed machine learning technique and has an aim of increasing the speed and efficiency. Unlike the trivial gradient boosting method, it performs the computations and creates the decision trees parallelly.
What does XGBoost perform so well?
XGBoost algorithm performs so well as compared to the GBM’s because XGBoost applies major enhancements over the principle of boosting weak learners using optimisation techniques and efficient algorithmic implementation. Some of the major improvements are as follows:
Parallel computation
XGBoost builds the decision tree classifier parallelly and it enhances the performance of the trivial GBM’s multifolds.
Tree pruning
XGBoost uses the tree pruning technique to limit the size of the decision tree classifier built. This technique is very useful in reducing the computational time and is implemented in a depth first search manner.
Regularisation
To avoid any kind of bias/overfitting, the L1 and L2 regularisation is included that contributes to the great results achieved by the algorithm.
Cross-validation
It uses cross validation which makes it much easier to implement and improve the accuracy of the predictions .
To show how efficient XGBoost is, we use the rainfall dataset from kaggle and used various models and the results are as follows
We can clearly see the performance of XGBoost as compared to other ML algorithms used for making predictions on the dataset.
Advantages and Disadvantages of Fuzzy Clustering
Advantages
Disadvantages
Efficient in terms of computational power, and gives great results most of the times when used in various predictions.
Has a potential chance of overfitting if parameters are not tuned properly.
Outliers are handled properly and less engineering is required while using XGBoost.
Interpretation becomes difficult at times and if some of the parameters are not tuned properly, we might not get great results.
Frequently Asked Questions
How is XGBoost useful? XGBoost is useful because it outperforms the other ML algorithms. It requires less computational time and requires less engineering effort. Also it is implemented such that it can perform on out-of-core computing.
Should we always use XGBoost? The answer to this question in the field of machine learning is uncertain, because we must try out all possible algorithms which can possibly lead to good results and according to many experienced machine learning engineers even the simplest of algorithms can turn out to be very efficient.
Key takeaways
This article gave a brief introduction to what XGBoost is. We saw what Boosting is and different types of Boosting algorithms. We explored XGBoost and saw why it performs so well than many other machine learning algorithms. To dive deeper into machine learning, check out our industry-level courses on coding ninjas.
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