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:
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.
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