By definition; Support vector machine (SVM) is an extension of the support vector classifier that results from enlarging the feature space in a specific way, using kernels. 📖
What new skills have you learned?
📦 Support Vector Machines
Support Vector Machines
Often referred to as SVM. SVM’s are supervised learning models with associated learning algorithms that analyze data and recognize patterns mainly used for classification and regression analysis.
🔅 An SVM model is a
representation of points in space mapped so that categories are divided by a clear gap that is as wide as possible.
💥 Mapped points in the same space are predicted to belong to the same category.
Determinant Components in SVM’s
- Choose a
hyperplane-(A flat affine subspace of dimension p - 1), that maximizes the margins between classes. The vector points that the margins lines touch are called support vectors
📌 C : A low C value equates to low Bias - high Variance
📌 gamma: small gamma results to gaussian with large Variance - High gamma results to high Bias-low Variance.
Sample notebook with iris dataset classification using SVM
So that was the Eighth week.. 🔏