Share 'Decision tree vs. linearly separable or non-separable pattern'
As a part of a series of posts discussing how a machine learning classifier works, I ran decision tree to classify a XY-plane, trained with XOR patterns or linearly separable patterns.
1. Simple (non-overlapped) XOR pattern
It worked well. Its decision boundary was drawn almost perfectly parallel to the assumed true boundary, i.e. XY axes.
2. Complex (overlapped) XOR pattern without pruning…
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