Supervised Learning - Classification Week 7 Challenge

ADVANTAGES:

• 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.

DISADVANTAGES:

• 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.