MLG 013 Shallow Algos 2

Apr 09, 2017
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Support Vector Machines (SVMs) and Naive Bayes classifiers are two powerful shallow learning algorithms used mainly for classification, with the capacity for regression as well. SVMs create decision boundaries to distinguish between categories by aiming to maximize this boundary's thickness (or margin) for optimal separation and resistance to overfitting, while Naive Bayes employs probabilistic reasoning and Bayesian inference to classify data based on assumed conditional independence of features.

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Support Vector Machines (SVM)

  • Purpose: Classification and regression.
  • Mechanism: Establishes decision boundaries with maximum margin.
  • Margin: The thickness of the decision boundary, large margin minimizes overfitting.
  • Support Vectors: Data points that the margin directly affects.
  • Kernel Trick: Projects non-linear data into higher dimensions to find a linear decision boundary.

Naive Bayes Classifiers

  • Framework: Based on Bayes' Theorem, applies conditional probability.
  • Naive Assumption: Assumes feature independence to simplify computation.
  • Application: Effective for text classification using a "bag of words" method (e.g., spam detection).
  • Comparison with Deep Learning: Faster and more memory efficient than recurrent neural networks for text data, though less precise in complex document understanding.

Choosing an Algorithm

  • Assessment: Evaluate based on data type, memory constraints, and processing needs.
  • Implementation Strategy: Apply multiple algorithms and select the best-performing model using evaluation metrics.

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