SVM stands for Support Vector Machine that is a machine learning approach used for classification and regression analysis. SVM is a supervised machine learning algorithm and mostly used in classification problems. This algorithm analyze the bulk amount of data to identify patterns from them.

As SVM is a fast and dependable classification algorithm that can perform very nicely with a limited amount of data.

To transform the data SVM uses a technique known as Kernel trick. By using the transformed data it finds an optimal boundary between the possible outputs. Moreover, sometimes it transforms the extremely complex data as well to figure out how to separate the data which is based on the given labels or outputs.

What are the positive and negative points of SVM algorithm?

Positive Points of SVM

• In comparison to the competitors, SVM is found to be more accurate.
• Due to optimal margin gap between separating hyper planes, SVM is found to be more robust.
• It can do better predictions with test data.
• As SVM uses Kernel trick in dual problem that's why it is computationally more efficient.
• SVM has regularization capabilities to prevent it from over-fitting.
• It can efficiently handles non-linear data by using Kernel trick.
• It is capable to solve both Classification and Regression problems. SVM (Support Vector Machine) used for classification problems whereas SVR (Support Vector Regression) is used for regression problems.
• SVM model is stable as a small change in the data does not affect the hyperplane.

Negative Points of SVM

• To handle the non-linear data, it is sometimes difficult to choose an appropriate Kernel function. In case if you use a high dimension Kernel, it may generate too many support vectors which reduces the training speed drastically.
• SVM needs extensive memory to store all the support vectors in the memory and it grows continuously according to the training dataset size.
• It requires feature scaling.
• For large datasets, it takes a long training time.
• SVM models are difficult to understand and to interpret.

Applications of SVM

SVM is a supervised learning algorithm and it is able to classify unseen data correctly. SVM has numerous applications in various fields and the apps are:

• Face Detection
• Bioinformatics
• Classification of images
• Text and hypertext categorization
• Handwriting recognition
• Protein fold and remote homology detection
• Generalized predictive control or GPC