SUPPORT
VECTOR
MACHINE
Data science using python
Supervised learning
What is SVM?
A Support Vector Machine (SVM) is a supervised machine learning algorithm
commonly used for classification and regression tasks. It’s known for its effectiveness,
especially in high-dimensional spaces, and works well when there is a clear margin of
separation between classes.
SVM is one of the most popular supervised learning algorithms, which is used for
Classification as well as Regression problems.
The goal of the SVM algorithm is to create the best line or decision boundary that can
segregate n dimensional space into classes so that we can easily put the new data point
in the correct category in the future.
SVM- Example
Hyperplane and Support vectors
The goal of the SVM algorithm is to create the best line or decision boundary that can
segregate n dimensional space into classes so that we can easily put the new data point
in the correct category in the future. This best decision boundary is called a hyperplane.
Support vectors are data points that are closer to the hyperplane and influence the
position and orientation of the hyperplane. Using these support vectors, we maximize
the margin of the classifier. Deleting the support vectors will change the position of the
hyperplane. These are the points that help us build our SVM
Understanding SVM
Steps in SVM
1.Necessary imports for svm
2.Load data from CSV file
3.Distribution of the classes
4.Selection of unwanted columns
5.Remove unwanted columns
6.Divide the data as train/test dataset
7.Modeling (SVM with Scikit-learn)
8.Evaluation
