build featureset and response class labels bc_x np array(bc_featuresbc_y np array(bc_classest[ print('feature set shape:'bc_x shapeprint('response class shape:'bc_y shapefeature set shape( response class shape( ,we can clearly see thatas we mentioned beforethere are total of features in this dataset and total of rows of observations to get some more detail into the feature names and take peek at the data pointsyou can use the following code in [ ]np set_printoptions(threshold= print('feature set data [shape'+str(bc_x shape)+']'print(np round(bc_x )'\ 'print('feature names:'print(np array(bc_features columns)'\ 'print('response class label data [shape'+str(bc_y shape)+']'print(bc_y'\ 'print('response variable name:'np array(bc_classes columns)np set_printoptions(threshold=ptfeature set data [shape( )[ ]feature names['mean radius'mean texture'mean perimeter'mean area'mean smoothness'mean compactness'mean concavity'mean concave points'mean symmetry'mean fractal dimension'radius error'texture error'perimeter error'area error'smoothness error'compactness error'concavity error'concave points error'symmetry error'fractal dimension error'worst radius'worst texture'worst perimeter'worst area'worst smoothness'worst compactness'worst concavity'worst concave points'worst symmetry'worst fractal dimension'response class label data [shape( ,)[ response variable name['ismalignant'this gives us better perspective on the data we are dealing with the response class variable is binary class where indicates the tumor detected was benign and indicates it was malignant we can also see the features that are real valued numbers that describe characteristics of cell nuclei present in digitized images of breast mass let' now use the chi-square test on this feature set and select the top best features out of the features the following snippet helps us achieve this