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Does SVM work well with categorical data?
Non-numerical data such as categorical data are common in practice. Among the three classification methods, only Kernel Density Classification can handle the categorical variables in theory, while kNN and SVM are unable to be applied directly since they are based on the Euclidean distances.
How do you change categorical data to numerical data in Python?
Another approach is to encode categorical values with a technique called “label encoding”, which allows you to convert each value in a column to a number. Numerical labels are always between 0 and n_categories-1. You can do label encoding via attributes . cat.
Can Knn take categorical data?
KNN is an algorithm that is useful for matching a point with its closest k neighbors in a multi-dimensional space. It can be used for data that are continuous, discrete, ordinal and categorical which makes it particularly useful for dealing with all kind of missing data.
Can a SVM be used for a Non-Linear DataSet?
As mentioned above SVM is a linear classifier which learns an (n – 1)-dimensional classifier for classification of data into two classes. However, it can be used for classifying a non-linear dataset. This can be done by projecting the dataset into a higher dimension in which it is linearly separable!
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.
How does SVM work in the case of imbalanced data?
How does SVM work in the case of an imbalanced dataset? Observe how SVM works in case of an imbalanced dataset. Observe how the hyperplane changes according to the change in the regularization term.
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.