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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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