3.4.4. Support Vector Machine
The objective of SVM is to find a line that best separates the data into
multiple groups. This is achieved by an optimization process supported
by the data in the training set. These instances are called as support
vectos and they form a crucial role in the classificiation process
[60]. Finally, few datasets can be separated with just a straight
line. Sometimes a line with curves or even polygonal regions needs to be
marked out. This is achieved with SVM by projecting the data into a
higher-dimensional space to draw the lines and make predictions. To make
predictions for the sign of containment, an SVM was modelled for 500
epochs and a learning rate of 0.01. The hinge loss function with
stochastic gradient descent was used to model the loss and batch size
was set to 10. The epsilon threshold was set to 0.001 and the
regularization constant was set to 10-4. The resultant
model produced an accuracy of 76.19% in classifying the dependent
variable. On a 5-fold cross-validation with the data, it can be inferred
that the random forest design produces the minimum error and maximum
accuracy as reported in Table 4. It outshines all the other machine
learning algorithms constructed in the study. J48 decision tree,
logistic regression and SVM produce almost similar levels of accuracy in
predicting the sign of containment of COVID-19.
Table 4. Accuracy Metrics for Machine Learning Models