APPLICATION OF SUPERVISED LEARNING
SUPERVISED LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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The data is linearly separable.
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The data is clean and ready to use.
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The data contains overlapping points.
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None of these
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Detailed explanation-1: -SVM does not perform very well when the data set has more noise i.e. target classes are overlapping. In cases where the number of features for each data point exceeds the number of training data samples, the SVM will underperform.
Detailed explanation-2: -Explanation: When the data has noise and overlapping points, there is a problem in drawing a clear hyperplane without misclassifying.
Detailed explanation-3: -1) SVMs are not suitable for large datasets The original SVM implementation is known to have a concrete theoretical foundation, but it is not suitable for classifying in large datasets for one straightforward reason-the complexity of the algorithm’s training is highly dependent on the size of the dataset.
Detailed explanation-4: -Unsuitable to Large Datasets. Large training time. More features, more complexities. Bad performance on high noise. Does not determine Local optima.