Support Vector Machines (SVMs) are a popular machine learning algorithm used for classification and regression tasks. SVMs work by finding the best hyperplane that separates the data into different classes. In Python’s Scikit-Learn library, there are several variants of SVMs that can be used for different types of data and tasks. In this article, we will provide a guide to using different SVM variants in Python’s Scikit-Learn.
1. Linear SVM
Linear SVM is the most basic variant of SVMs. It works by finding the best hyperplane that separates the data into different classes. Linear SVM is suitable for linearly separable data, where the classes can be separated by a straight line. In Scikit-Learn, the LinearSVC class can be used to implement linear SVM.
To use LinearSVC, first, we need to import it from Scikit-Learn:
“`python
from sklearn.svm import LinearSVC
“`
Next, we need to create an instance of LinearSVC and fit it to our data:
“`python
clf = LinearSVC()
clf.fit(X_train, y_train)
“`
Here, X_train is the training data and y_train is the corresponding labels. Once the model is trained, we can use it to predict the labels of new data:
“`python
y_pred = clf.predict(X_test)
“`
2. Polynomial SVM
Polynomial SVM is used for non-linearly separable data. It works by transforming the data into a higher-dimensional space using a polynomial kernel function. In Scikit-Learn, the SVC class can be used to implement polynomial SVM.
To use SVC with a polynomial kernel, we need to specify the kernel parameter as ‘poly’ and set the degree parameter to the degree of the polynomial:
“`python
from sklearn.svm import SVC
clf = SVC(kernel=’poly’, degree=3)
clf.fit(X_train, y_train)
“`
Here, degree=3 means that we are using a third-degree polynomial kernel. We can experiment with different values of the degree parameter to find the best value for our data.
3. Radial Basis Function (RBF) SVM
RBF SVM is another variant of SVMs used for non-linearly separable data. It works by transforming the data into a higher-dimensional space using a radial basis function kernel. In Scikit-Learn, the SVC class can be used to implement RBF SVM.
To use SVC with an RBF kernel, we need to specify the kernel parameter as ‘rbf’:
“`python
clf = SVC(kernel=’rbf’)
clf.fit(X_train, y_train)
“`
The RBF kernel has a gamma parameter that controls the width of the Gaussian function used in the kernel. We can experiment with different values of gamma to find the best value for our data.
4. Nu-Support Vector Classification (NuSVC)
NuSVC is a variant of SVMs that uses a parameter called nu instead of C to control the trade-off between the margin and the number of support vectors. In Scikit-Learn, the NuSVC class can be used to implement NuSVC.
To use NuSVC, we need to create an instance of NuSVC and fit it to our data:
“`python
from sklearn.svm import NuSVC
clf = NuSVC()
clf.fit(X_train, y_train)
“`
NuSVC has a parameter called nu that controls the upper bound on the fraction of training errors and the lower bound on the fraction of support vectors. We can experiment with different values of nu to find the best value for our data.
Conclusion
In this article, we provided a guide to using different SVM variants in Python’s Scikit-Learn. We covered Linear SVM, Polynomial SVM, RBF SVM, and NuSVC. Each variant is suitable for different types of data and tasks. By experimenting with different variants and parameters, we can find the best SVM model for our data.
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