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Support-Vector Machines - Why did SVM fail?
Answer
The SVM algorithm's downfall stems from its incapacity to efficiently handle enormous datasets. When the target classes overlap or there is a lot of noise in the dataset, its performance usually goes worse. The SVM's performance will also be less than ideal if there are more features per data point than there are samples of training data.
