Gradient Boosting Machine is an ensemble method in machine learning. It uses a series of weak models sequentially to improve the model accuracy based on the errors of the previous model. The idea is to find out if there is any pattern in the residuals or errors.
In this tutorial, you will get the intuition on how the gradient boosting machine works with an example and then we will develop a regression model using the gradient boosting machine in Python's sklearn library.
Here is the documentation on the Gradient Boosting Machine:
scikit-learn.org/stable/modul...
The dataset used in this tutorial is here:
github.com/rashida048/Machine...
The complete code used in this tutorial can be found here:
github.com/rashida048/Machine...
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