PhD Candidate | Software Developer | Passionate about Machine Learning and Software Engineering
Nowadays, most people use scikit-learn for machine learning projects. Because scikit-learn is a top quality ML package for Python and lets you use a machine learning algorithm in several lines of Python code, which is great!
As a machine learning researcher, I personally like to try and use other machine learning libraries. It’s good to have knowledge of other ML libraries in your arsenal. Since I used C++ for my projects, I decided to try a C++ machine learning library.
Support Vector Machine (SVM) is a popular and state-of-the-art classification algorithm. Hence many packages and implementations of standard SVM can be found on the internet. However, there are some interesting extensions of SVM that has a slightly better prediction accuracy. Of these extensions, Twin Support Vector Machine (TSVM) has received more attention from scholars in the field of SVM research. Even I myself have published a classifier based on TSVM and KNN.
TSVM does classification using two non-parallel hyperplanes as opposed to a single hyperplane in standard SVM (To know more about TSVM, you can read this blog post.). Unlike SVM, TSVM had almost no fast and simple implementation on the internet prior to 2018. So I decided to develop the LightTwinSVM program and share it with others for free.