Extreme Gradient Boosting Classification

Extreme Gradient Boosting, most popularly known as XGBoost is a gradient boosting algorithm that is used for both classification and regression problems. XGBoost is a star among hackathons as a winning algorithm. XGBoost provides a parallel tree boosting that solve many data science problems in a fast and accurate way.

For a deeper understanding of XGB Classification, use the following resources:

• XGBoost: A Scalable Tree Boosting System
• XGBoost Documentation

In this practise session, we will learn to code XGB Classifier. We will perform the following steps to build a simple classifier using the popular Iris dataset. You can find the dataset here.

Step 1. Data Preprocessing 

• Importing the libraries.
• Importing dataset (Dataset Link https://archive.ics.uci.edu/ml/datasets/iris).
• Dealing with the categorical variable.
• Classifying dependent and independent variables.
• Splitting the data into a training set and test set.
• Feature scaling.

Step 2. XGB Classification 

• Create a XGB classifier.
• Feed the training data to the classifier.
• Predicting the species for the test set.
• Using the confusion matrix to find accuracy.