From the course: Machine Learning and AI: Advanced Decision Trees with SPSS

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XGBoost Linear

XGBoost Linear

From the course: Machine Learning and AI: Advanced Decision Trees with SPSS

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XGBoost Linear

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- [Instructor] Here we go. We're going to stay in the same stream. And under the Python tab, I've got XG Boost Linear, which is implemented through Python. I'm going to bend this arrow over here because I don't need anything coming out of the cart predictor node there. And it's all set up based on my type node. I've got, name has been discarded. Miles per gallon is my target. I've got my predictors. And under Build Options, there are a lot of parameters that take quite a bit of knowledge of the XG Boost algorithm to adjust. We do have the option of having it do a parameter search by checking off this box here. And when we do that, everything is grayed out. But for this first attempt, we're just going to go ahead and turn that off and let it run on defaults. So let that run. I'll drag this into place here. This is a simple dataset, so let's see how they do. Going to run my analysis node. And mean absolute error is…

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