you can see that the two classes separate quite well in this two-dimensional space this leads us to believe that even linear classifier (that would learn line in this spacecould do reasonably good job at distinguishing the two classes we can also see that the malignant (redpoints are more spread out than the benign (bluepoints --something that we could already see bit from the histograms in figure - downside of pca is that the two axes in the plot are often not very easy to interpret the principal components correspond to directions in the original dataso they are combinations of the original features howeverthese combinations are usually very complexas we'll see shortly the principal components themselves are stored in the components_ attribute of the pca object during fittingin[ ]print("pca component shape{}format(pca components_ shape)out[ ]pca component shape( each row in components_ corresponds to one principal componentand they are sorted by their importance (the first principal component comes firstetc the columns correspond to the original features attribute of the pca in this example"mean radius,"mean texture,and so on let' have look at the content of components_in[ ]print("pca components:\ {}format(pca components_)out[ ]pca components[ [- - - - - - - - - - - - - ]we can also visualize the coefficients using heat map (figure - )which might be easier to understandin[ ]plt matshow(pca components_cmap='viridis'plt yticks([ ]["first component""second component"]plt colorbar(plt xticks(range(len(cancer feature_names))cancer feature_namesrotation= ha='left'plt xlabel("feature"plt ylabel("principal components" unsupervised learning and preprocessing