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Choosing the Right Algorithm
- Evaluate your data, system memory, and processing constraints before selecting an algorithm.
- Implement and test multiple classifiers, then choose the best-performing model based on evaluation metrics.
Support Vector Machine Margin
- Support Vector Machines build a decision boundary with maximum margin to reduce overfitting.
- The margin is the thickness of this boundary, and the closest data points that define it are called support vectors.
Kernel Trick for Non-Linearity
- The kernel trick transforms data into higher dimensions to make it linearly separable.
- This lets Support Vector Machines handle non-linear classification effectively.