Friday, 1 March 2019

Advantages and Disadvantages of SVM (Support Vector Machine) in Machine Learning

SVM (Support Vector Machine) classifies the data using hyperplane which acts like a decision boundary between different classes. Extreme data points from each class are called Support Vectors. SVM tries to find the best and optimal hyperplane which has maximum margin from each Support Vector. 

Kernel functions / tricks are used to classify the non-linear data. It transforms non-linear data into linear data and then draws a hyperplane. 

Below are the advantages and disadvantages of SVM:

Advantages of Support Vector Machine (SVM)

1. Regularization capabilities: SVM has L2 Regularization feature. So, it has good generalization capabilities which prevent it from over-fitting.

2. Handles Non-linear data efficiently: SVM can efficiently handle non-linear data using Kernel trick.

3. Solves both Classification and Regression problems: SVM can be used to solve both classification and regression problems. SVM is used for classification problems while SVR (Support Vector Regression) is used for regression problems.

4. Stability: A small change to the data does not greatly affect the hyperplane and hence the SVM. So the SVM model is stable.

Disadvantages of Support Vector Machine (SVM)

1. Choosing an appropriate Kernel function: Choosing an appropriate Kernel function (to handle the non-linear data) is not an easy task. It could be tricky and complex. In the case of using a high dimension Kernel, you might generate too many support vectors which reduce the training speed drastically. 

2. Extensive memory requirement: Algorithmic complexity and memory requirements of SVM are very high. You need a lot of memory since you have to store all the support vectors in the memory and this number grows abruptly with the training dataset size.

3. Long training time: SVM takes a long training time on large datasets.

4. Difficult to interpret: SVM model is difficult to understand and interpret by human beings unlike Decision Trees.

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