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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.

Hackathon Reviews


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  1. XGBoost nutshell


    This was really helpful to understand the tree boosting technique regarding the coding aspect.
    and the role of tree boosting in enhancing the performance of the algorithm.


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