XGBoost in Python (Quickly) Explained (3min Read)

XGBoost is an algorithm used for supervised learning problems.

It means Extreme Gradient Boosting.

How It works?

You have to imagine a sequence of models and each model is trained from the error of its predecessor.

Where It is applied?

Classification And Regression Trees  is the base learner and you apply a gradient boosted trees.

Is an iterative process where in each iteration you train a new tree, based on past “informations”.

For example, you have N models, the simplified logical scheme is the following:

• Train (X,y)
• Tree1
• Predict
• Define the error r1=y1-y*1
• Train (X,r1)
• Tree 2
• Predict
• Define the new error r2=r1-r*1

OOOOOOO

• Train (X, rn-1)
• Tree N
• Predict
• Define the last error rn=rn-1-rn-1*

Here a personal draw to understand better:

Library for Python

Sklearn (here the official documentation, read it if you have time! If you don’t find it!) has the specific class:

When to use it?

There are several kaggle competitions that were won using XGBoost algorithm, which competition?

1. Avito Challenge on “Predict demand for an online classified ad”- Binary Classification Problem
2. Otto Group Challenge “Classify products into the correct category” –Multi-label classification problem
3. Rossman “Forecast sales using store, promotion, and competitor data “ –Regression Problem

Dark Side

You must be careful about overfitting.

Gradient boosted trees are quick to learn.

To avoid Overfitting you can use shrinkage also called learning rate.

Applying a weighting factor for every new tree in our sequence is a validated way to slow learning in gradient boosting.

How in Python?

You can find a lot of tutorial here on the official skelearn documentation, the “core process” is the following:

To tune the shrinkage there is a specific argument inside the class:

“learning_rate : float, optional (default=0.1):

learning rate shrinks the contribution of each tree by learning_rate”

Want you to go deeper?

Read this interesting Q/A on stack overflow and the first paper on Gradient Boosting at this link ,tough paper.

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If you think something needs to be fixed, you found any typo, write me!

Thanks for reading my article!

Andrea

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