Vs (SVM & Logistic Regression)

Description

• SVM tries to find the "best" margin (distance between the line and the support vectors) that separates the classes and this reduces the risk of error on the data, while logistic regression does not, instead it can have different decision boundaries with different weights that are near the optimal point.

  

• SVM works well with unstructured and semi-structured data like text and images while logistic regression works with already identified independent variables.

• SVM is based on geometrical properties of the data while logistic regression is based on statistical approaches.
• The risk of overfitting is less in SVM, while Logistic regression is vulnerable to overfitting.