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.. 🔏