3. Kernel & Non-Parametric Methods
Advanced machine learning methods enable the modeling of complex and non-linear relationships in data. Kernel methods, such as support vector machines, utilize high-dimensional feature spaces through the kernel trick, enhancing flexibility and accuracy. Non-parametric models such as k-Nearest Neighbors, Parzen Windows, and Decision Trees provide adaptability without assuming a fixed form, although they require careful parameter tuning and are sensitive to noise and high-dimensionality.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Not all patterns can be captured using simple linear models, necessitating advanced methods.
- Kernel methods allow for efficient computation in high-dimensional spaces while maintaining flexibility.
- Non-parametric methods grow with data and avoid fixed parameter assumptions, making them powerful for various applications.
Key Concepts
- -- Kernel Trick
- A technique that allows for implicit mapping of input features to high-dimensional spaces to facilitate linear separation.
- -- Support Vector Machine (SVM)
- A supervised learning algorithm that finds the hyperplane that maximizes the margin between classes.
- -- kNearest Neighbors (kNN)
- A non-parametric method that classifies a data point based on the majority label of its k nearest neighbors.
- -- Decision Trees
- A model that makes decisions based on feature-based splits to reduce impurity and improve classification.
Additional Learning Materials
Supplementary resources to enhance your learning experience.