MLG 013 Shallow Algos 2

MLG 013 Shallow Algos 2

Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/13

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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