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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You want to increase your data points
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You want to decrease your data points
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You will try to calculate more variables
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You will try to reduce the features
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Detailed explanation-1: -Suppose you have trained an SVM with a linear decision boundary after training SVM, you correctly infer that your SVM model is underfitting. 16) Which of the following option would you more likely to consider iterating SVM next time? The best option here would be to create more features for the model.
Detailed explanation-2: -Linear kernel SVC and Logistic Regression can produce the same decision boundary, that is the exact same model, and therefore the exact same performance metrics, despite using entirely different methods.
Detailed explanation-3: -In SVM, to avoid overfitting, we choose a Soft Margin, instead of a Hard one i.e. we let some data points enter our margin intentionally (but we still penalize it) so that our classifier don’t overfit on our training sample. Here comes an important parameter Gamma (), which control Overfitting in SVM.