Supervised Learning - Classification Week 7 Challenge

Q.1:-

PROS OF SVM:

SVM classifiers perform well in high-dimensional space and have excellent accuracy. SVM classifiers require less memory because they only use a portion of the training data.

SVM performs reasonably well when there is a large gap between classes.

High-dimensional spaces are better suited for SVM.

When the number of dimensions exceeds the number of samples, SVM is useful.

SVM uses memory effectively.

CONS OF SVM:

SVM requires a long training period; as a result, it is not practical for large datasets.

The inability of SVM classifiers to handle overlapping classes is another drawback.

Large data sets are not a good fit for the SVM algorithm.

When the data set contains more noise, such as overlapping target classes, SVM does not perform as well.

The SVM will perform poorly when the number of features for each data point is greater than the number of training data samples.

Q.2:- Performed in file, please find the attached file.