Support Vector Machine (SVM)
(เครื่องเวกเตอร์สนับสนุน (SVM))
Definition
Support Vector Machine (SVM) (เครื่องเวกเตอร์สนับสนุน (SVM)) Hard Skill
Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks that finds the optimal hyperplane to separate different classes in a dataset.
Expertise Level
Level 1
Basic
1. Understands the fundamental concepts of SVM including hyperplanes and support vectors.
2. Can implement basic SVM models using standard machine learning libraries.
3. Recognizes the differences between linear and non-linear classification.
Level 2
Intermediate
1. Can tune SVM hyperparameters such as kernel type, C value, and gamma for improved model performance.
2. Applies SVM to multi-class classification problems using strategies like one-vs-one or one-vs-rest.
3. Understands kernel methods and their role in transforming data for non-linear separation.
Level 3
Advanced
1. Designs custom kernel functions tailored to specific datasets and problems.
2. Integrates SVM within complex machine learning pipelines and ensembles.
3. Analyzes and optimizes SVM model performance on large scale or high-dimensional data.
4. Understand theoretical aspects such as margin maximization and support vector optimization.
