APPLICATION OF SUPERVISED LEARNING
SUPPORT VECTOR MACHINE SVM
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Hyperplane
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Associative
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Clustering points
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Distance
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Detailed explanation-1: -What makes the linear SVM algorithm better than some of the other algorithms, like k-nearest neighbors, is that it chooses the best line to classify your data points. It chooses the line that separates the data and is the furthest away from the closet data points as possible.
Detailed explanation-2: -It is robust to outliers. Decision model can be easily updated. It has excellent generalization capability, with high prediction accuracy. Its implementation is easy. 03-Oct-2020
Detailed explanation-3: -Add More Data. Having more data is always a good idea. Treat Missing and Outlier Values. Feature Engineering. Feature Selection. Multiple Algorithms. Algorithm Tuning. Ensemble Methods. Cross Validation. 29-Dec-2015