SVM Support Vector Machine What it is and why it's important By, SUGAPRIYAN P K 2020102145 20CS205 SVM Explained What you need to know Introduction to Support Vector Machines How SVM works? Advantages of SVM Disadvantages of SVM Applications of SVM Conclusion Intro to SVM Support Vector Machines (SVM) is a machine learning algorithm used for classification and regression analysis. It is based on the idea of finding a hyperplane that separates the data points in different classes with maximum margin. SVM is particularly useful when dealing with complex datasets where linear separation is not possible. It can also handle high-dimensional data and can be used for both binary and multi-class classification. Hyperplane There can be multiple lines/decision boundaries to segregate the classes in n-dimensional space, but we need to find out the best decision boundary that helps to classify the data points. This best boundary is known as the hyperplane of SVM. Support Vectors There can be multiple lines/decision boundaries to segregate the classes in n-dimensional space, but we need to find out the best decision boundary that helps to classify the data points. This best boundary is known as the hyperplane of SVM. How SVM Works? SVM algorithm is by focusing on its primary type, the SVM classifier. The idea behind the SVM classifier is to come up with a hyper-lane in an N-dimensional space that divides the data points belonging to different classes. SVM works by mapping data to a highdimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data are transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong. Advantages of SVM SVM Benefits HIGHLY SOPHISTICATED DYNAMIC ALGORITHM CONCEPT OF MARGIN Being a highly sophisticated and mathematically sound algorithm, it is one of the most accurate machine learning algorithms. It is a dynamic algorithm and can solve a range of problems, including linear and non-linear problems, binary, binomial, and multi-class-classification problems, along with regression problems. SVM uses the concept of margins and tries to maximize the differentiation between two classes; it reduces the chances of model overfitting, making the model highly stable. DISADVANTAGES OF SVM SVM can also be computationally expensive. LONG TIME FOR TRAINING it still fails in front of Naïve Bayes, providing faster predictions in high dimensions. Also, it takes a relatively long time during the training phase. Many a time before SVM modeling you may also have use dimension reduction techniques like Factor analysis or PCA HIGHLY SENSITIVE Like some other machine learning algorithms, which are often highly sensitive towards some of their hyper-parameters, SVM’s performance is also highly dependent upon the kernel chosen by the user. NOT HIGHLY INTERPRETABLE Compared to other linear algorithms such as Linear Regression, SVM is not highly interpretable, especially when using kernels that make SVM non-linear. Thus, it isn’t easy to assess how the independent variables affect the target variable. IMPLEMENTATION OF SVM With all its advantages and disadvantages, SVM is a widely implemented algorithm. Support vector machine examples include its implementation in image recognition, such as handwriting recognition and image classification. Other implementation areas include anomaly detection, intrusion detection, text classification, time series analysis, and application areas where deep learning algorithms such as artificial neural networks are used. Support vector machines are the most robust and accurate classification algorithms available, and have shown superior performance in numerous applications Applications of SVM SVM has a wide range of applications in various fields, including computer vision, natural language processing, bioinformatics, and finance. In computer vision, SVM can be used for image classification, object detection, and face recognition. In natural language processing, SVM can be used for sentiment analysis and text classification. In finance, SVM can be used for credit scoring and fraud detection Conclusion Support Vector Machines is a powerful machine learning algorithm that can handle complex datasets and achieve good generalization performance. However, it requires careful selection of kernel function and hyperparameters, and can be computationally expensive for large datasets. Despite its limitations, SVM has numerous practical applications in various fields and continues to be an active area of research in machine learning.
