SVM works by finding a hyperplane in an N-dimensional space (N number of features) which fits to the multidimensional data while considering a margin. - machinelearning datascience
In classification, same hyperplane is calculated but to distinctly classify the data points again while considering a margin. There can be many possible hyperplanes that could be selected. However, the objective is to find the hyperplane with the maximum margin, meaning maximum distance between between the target classes.
SVR can of course also be used for complicated datasets with more features by using higher dimensional feature terms similar to that in polynomial regression. import numpy as np from sklearn.svm import SVR import matplotlib.pyplot as plt np.random.seed X=np.sort, axis=0) T=np.linspace[:, np.newaxis] y=np.sin.ravel # Add noise to targets y[::5] +=1 * ) # Fit regression model SVR_rbf=SVR SVR_lin=SVR SVR_poly=SVR y_rbf=SVR_rbf.fit.predict y_lin=SVR_lin.fit.predict y_poly=SVR_poly.fit.predict # look at the results plt.scatter plt.plot plt.plot plt.plot plt.xlabel plt.ylabel plt.title plt.legend plt.
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