XGBoost With Python

Fisseha Berhane looked at Extreme Gradient Boosting with R and now covers it in Python:

In both R and Python, the default base learners are trees (gbtree) but we can also specify gblinear for linear models and dart for both classification and regression problems.
In this post, I will optimize only three of the parameters shown above and you can try optimizing the other parameters. You can see the list of parameters and their details from the website.

It’s hard to overstate just how valuable XGBoost is as an algorithm.

Related Posts

Neural Networks Are Polynomial Regression

Norman Matloff announces a new paper: A summary of the paper is: We present a very simple, informal mathematical argument that neural networks (NNs) are in essence polynomial regression (PR). We refer to this as NNAEPR. NNAEPR implies that we can use our knowledge of the “old-fashioned” method of PR to gain insight into how […]

Read More

Using DALEX To Explain Black-Box Models

Przemyslaw Biecek explains that there’s more than LIME for explaining black-box models: I’ve heard about a number of consulting companies, that decided to use simple linear model instead of a black box model with higher performance, because ,,client wants to understand factors that drive the prediction’’. And usually the discussion goes as following: ,,We have tried LIME […]

Read More


March 2018
« Feb Apr »