remark (scaling and penalty parameterswhen using radial basis function in svc in sklearnthe scaling ( can be set via the parameter gamma note that large values of gamma lead to highly peaked predicted functionsand small values lead to highly smoothed predicted functions the parameter in svc refers / in ( classification with scikit-learn in this section we apply several classification methods to real-world data setusing the python module sklearn (the package name is scikit-learnspecificallythe data is obtained from uci' breast cancer wisconsin data set this data setfirst published and analyzed in [ ]contains the measurements related to images of benign and malignant breast masses the goal is to classify breast mass as benign or malignant based on featuresradiustextureperimeterareasmoothnesscompactnessconcavityconcave pointssymmetryand fractal dimension of each mass the meanstandard errorand "worstof these attributes were computed for each imageresulting in features for instancefeature is mean radiusfeature is radius sefeature is worst radius the following python code reads the dataextracts the response vector and model (featurematrix and divides the data into training and test set skclass py from numpy import genfromtxt from sklearn model_selection import train_test_split url "http :/mlr cs umass edu/ml/machine -learning databases /url "breast -cancer wisconsin /name "wdbc datadata genfromtxt (url url name delimiter =','dtype =stry data [, responses data [, :astype ('float 'features as an ndarray matrix x_train x_test y_train y_test train_test_split xytest_size random_state to visualize the data we create scatterplot for the features mean radiusmean textureand mean concavitywhich correspond to the columns and of the model matrix figure suggests that the malignant and benign breast masses could be well separated using these three features skclass py from skclass import xy import matplotlib pyplot as plt from mpl_toolkits mplot import axes import numpy as np bidx np where ( =' 'midxnp where ( =' 'plot features radius column texture ( concavity (