Contents
Why do we use feature scaling in SVM?
In other words, training an SVM over the scaled and non-scaled data leads to the generation of different models. The two most widely adopted approaches for feature scaling are normalization and standardization. Normalization maps the values into the [0, 1] interval:
How to deal with an SVM with categorical attributes?
If you are sure the categorical attribute is actually ordinal, then just treat it as numerical attribute. If not, use some coding trick to turn it into numerical attribute. According to the suggestion by the author of libsvm, one can simply use 1-of-K coding.
What are algorithms need feature scaling, beside from?
I am working with many algorithms: RandomForest, DecisionTrees, NaiveBayes, SVM (kernel=linear and rbf), KNN, LDA and XGBoost. All of them were pretty fast except for SVM. That is when I got to know that it needs feature scaling to work faster. Then I started wondering if I should do the same for the other algorithms.
How to use categorical predictors in SVM and KNN?
For kNN classification, I use knn function from class package after all categorical variables are encoded to dummy variables. The parameter k is obtained by tune.knn function by 10-fold cross validation. The classification result is shown below.
How are support vectors used in a SVM?
Support Vector Machine (SVM) Support vectors Maximize margin. •SVMs maximize the margin (Winston terminology: the ‘street’) around the separating hyperplane. •The decision function is fully specified by a (usually very small) subset of training samples, the support vectors.
Which is a property of the SVM classifier?
Training an SVM classifier includes deciding on a decision boundary between classes. This boundary is known to have the maximum distance from the nearest point on each data class. Due to this property, SVM is also referred to as a maximum-margin classifier:
Which is the decision function of a SVMs?
•SVMs maximize the margin (Winston terminology: the ‘street’) around the separating hyperplane. •The decision function is fully specified by a (usually very small) subset of training samples, the support vectors. •This becomes a Quadratic programming problem that is easy to solve by standard methods.