Which algorithm creates a hyperplane to separate data points?

Question

BlackTom AI · Accepted Answer

To determine which algorithm creates a hyperplane to separate data points, we need to recall what each method does.
Option 1: Naive Bayes. This classifier uses probabilistic methods based on Bayes' theorem and assumes feature independence; it does not construct a hyperplane, so it’s not about separating data with a hyperplane.
Option 2: Decision Tree. A decision tree splits the feature space using axis-aligned thresholds to create branches, not a single global hyperplane; thus it doesn’t create a hyperplane separator.
Option 3: Random Forest. This is an ensemble of decision trees and inherits the same splitting principle as trees themselves; there is no single hyperplane that separates data, so this option doesn’t fit the description.
Option 4: Support Vector Machine (SVM). SVMs explicitly aim to find the optimal hyperplane that maximally separates classes in the feature space, using support vectors near the boundary. This is the method that creates a hyperplane to partition data.
So, the statement describing an algorithm that constructs a hyperplane to separate data points correctly matches the Support Vector Machine (SVM) approach. The other options rely on tree-based or probabilistic methods that do not hinge on a separating hyperplane.

Which algorithm creates a hyperplane to separate data points?

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