Johdanto Support Vector Machines -motivaatio ja perusteet

Opettele peruskäsitteitä, joilla Support Vector Machine (SVM) muodostaa voimakkaan lineaarisen luokittelijan. 1. **Data Preparation**: Before applying SVM, it's essential to preprocess your data. This includes normalizing or scaling your features and handling missing values. 2. **Linear vs. Non-Linear Classification**: SVM can be used for both linear and non-linear classification. In linear classification, the decision boundary is a straight line, while in non-linear classification, the decision boundary is a curve. 3. **Hard Margin vs. Soft Margin**: SVM can be applied with a hard margin or a soft margin. A hard margin tries to find a decision boundary that perfectly separates the classes, while a soft margin allows for some misclassifications