SVM Algorithm
Support Vector Machine (SVM) is a powerful and widely-used supervised machine learning algorithm for classification and regression tasks. It is based on the idea of finding the best hyperplane (a decision boundary) that separates data points into different classes with maximum margin. The algorithm works by transforming the input data into a higher-dimensional space, where a linear decision boundary can divide the data into distinct classes. This transformation is done using kernel functions, which enable SVM to handle complex, non-linear relationships between features and labels. SVM aims to minimize the classification error while maximizing the geometric margin, which results in a robust and accurate model.
The main strength of the SVM algorithm lies in its ability to handle high-dimensional data and its adaptability to different types of data distributions, thanks to the kernel trick. The algorithm is particularly effective when the number of features is much larger than the number of samples. One of the critical aspects of SVM is the selection of the appropriate kernel function and its parameters, which can significantly influence the performance of the model. Regularization is another crucial aspect of SVM, as it helps prevent overfitting by controlling the trade-off between maximizing the margin and minimizing the classification error. Despite its high accuracy and robustness, SVM can be computationally intensive and slow in training, particularly with large datasets.
library(e1071)
x <- cbind(x_train,y_train)
# Fitting model
fit <-svm(y_train ~ ., data = x)
summary(fit)
# Predict Output
predicted= predict(fit,x_test)
