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A Decision Tree trained on a 2D feature space produces decision boundaries that ...

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A Decision Tree trained on a 2D feature space produces decision boundaries that are:

Diagonal lines at arbitrary angles through the feature space

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Axis-parallel (rectilinear) rectangles, because each split thresholds on a single feature

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Smooth curved boundaries similar to SVMs with RBF kernels

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Circular boundaries centred on cluster centroids

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