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Can you compare LR and SVM?
SVM is deterministic (but we can use Platts model for probability score) while LR is probabilistic. For the kernel space, SVM is faster (stores just support vectors)
How do you predict SVM?
First, import the SVM module and create support vector classifier object by passing argument kernel as the linear kernel in SVC() function. Then, fit your model on train set using fit() and perform prediction on the test set using predict() .
Which is better logistics or SVM?
SVM try to maximize the margin between the closest support vectors whereas logistic regression maximize the posterior class probability. For the kernel space, SVM is faster.
Which is the alternative method to the primal form of SVM?
The above-discussed formulation was the primal form of SVM . The alternative method is dual form of SVM which uses Lagrange’s multiplier to solve the constraints optimization problem. If αi>0 then Xi is a Support vector and when αi=0 then Xi is not a support vector.
When do you use SVM in machine learning?
SVM is used to classify inputs (more on inputs later) into one of the predefined classes (like Yes / No or Head / Tail). If SVM is used to classify two classes like head / tail, then such a classifier is binary classifier otherwise it becomes multiclass classifier
What are the support vector points in SVM?
Terminologies used in SVM: The points closest to the hyperplane are called as the support vector points and the distance of the vectors from the hyperplane are called the margins.
What are the equations of a hard margin SVM?
Hard margin SVM: Assume 3 hyperplanes namely (π, π+, π−) such that ‘π+’ is parallel to ‘π’ passing through the support vectors on the positive side and ‘π−’ is parallel to ‘π’ passing through the support vectors on the negative side. the equations of each hyperplane can be considered as: