Which algorithm creates a hyperplane to separate data points?

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Question restatement: Which algorithm creates a hyperplane to separate data points?
Option 1: 'Support Vector Machine (SVM)'. This choice aligns with the core idea of SVMs, which seek a decision boundary (hyperplane) that maximally separates classes by maximizing the margin between support vectors. The hyperplane is explicitly defined in the feature space to distinguish different classes.
Option 2: 'Naive Bayes'. Naive Bayes is a probabilistic classifier based on Bayes' theorem with strong (naive) independence assumptions among features. It does not construct a geometric separating hyperplane; instead, it computes posterior probabilities for class membership given the feature values.
Option 3: 'Random Forest'. Random Forest is an ensemble method that builds many decision trees and combines their outputs. While trees partition the feature space, they do so with axis-aligned splits that create a collection of regions rather than a single explicit separating hyperplane across the space.
Option 4: 'Decision Tree'. A single decision tree likewise performs recursive axis-aligned splits to partition space into regions. Although it can separate classes, it does so through a tree of thresholds rather than by identifying a single global hyperplane that divides the data.
Additional note: Conceptually, the hyperplane is a linear decision boundary characteristic of linear classifiers, of which SVMs (in their linear form) are the canonical example. The other algorithms operate via probabilistic reasoning (Naive Bayes) or hierarchical axis-aligned partitioning in a tree ensemble (Random Forest) or a single tree (Decision Tree), which do not rely on a hyperplane as their primary separating boundary.
In summary, among the options provided, the method that explicitly constructs and optimizes a hyperplane to separate data points is tied to the Support Vector Machine approach, while the others use different mechanisms for classification.

Which algorithm creates a hyperplane to separate data points?

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