Supervised Learning - Classification Week 7 Challenge
1.
| Advantages | Disadvantages |
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SVM works relatively work when there is a clear margine of separation between classes. |
SVM algorithm is not suitable for large datasets. |
| SVM is more effective in high dimensional spaces. | SVM doesnot perform very well when the data set has more noise i.e, target classes are overlapping. |
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SVM is effective in cases where the number of dimensions is greater than the number of samples. |
In cases where the number of features for each data point exceeds the number of training data samples, the SVM will underperform. |
| SVM is relatively memory efficient. | As the support vector classifier works by putting data points, above and below the classifying hyperplane there is no probabilistic explanation for the classification. |