We're planning to conduct a data mining study that's focused on using decision trees. After creating a decision tree model and feeding it test data, there's bound to be results.
How can I visualize the results? Especially the comparison of the predicted data and the actual data. And would it be possible to create a 3D visualization?
Especially if you're using a tree ensemble model like a random forest or boosted trees, I would check out SHAP (https://github.com/slundberg/shap) as a toolkit for understanding the model predictions. For comparing predictions vs. correct labels, something like a receiver operating characteristic (ROC) curve should be helpful, through I don't know of a 3D visualization for performance off the top of my head.
Hi,
I'm not sure, but Orange could create the decision tree, plot it and you can predict resultat and compare with actual data.
https://orange.biolab.si/screenshots/
Decision trees are the building blocks of some of the most powerful supervised learning methods that are used today. A decision tree is basically a binary tree flowchart where each node splits a group of observations according to some feature variable. The goal of a decision tree is to split your data into groups such that every element in one group belongs to the same category. Decision trees can also be used to approximate a continuous target variable. In that case, the tree will make splits such that each group has the lowest mean squared error.
More https://medium.com/@rnbrown/creating-and-visualizing-decision-trees-with-python-f8e8fa394176
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