the output of the above command is the names of the features for label benign cancermean texture we can print the features for these labels with the help of following commandprint(features[ ]this will give the following output[ + + + + - - - - - - + - + + - - - - - - + + + + - - - - - - we can print the features for these labels with the help of following commandprint(features[ ]this will give the following output[ + + + + - - - - - - - - + + - - - - - - + + + + - - - - - - step organizing data into training testing sets as we need to test our model on unseen datawe will divide our dataset into two partsa training set and test set we can use train_test_split(function of sklearn python package to split the data into sets the following command will import the functionfrom sklearn model_selection import train_test_split nownext command will split the data into training testing data in this examplewe are using taking percent of the data for testing purpose and percent of the data for training purposetraintesttrain_labelstest_labels train_test_split(features,labels,test_size random_state