nesses of each algorithmand what kind of data they can best be applied to we will also explain the meaning of the most important parameters and options many algorithms have classification and regression variantand we will describe both it is not necessary to read through the descriptions of each algorithm in detailbut understanding the models will give you better feeling for the different ways machine learning algorithms can work this can also be used as reference guideand you can come back to it when you are unsure about the workings of any of the algorithms some sample datasets we will use several datasets to illustrate the different algorithms some of the datasets will be small and synthetic (meaning made-up)designed to highlight particular aspects of the algorithms other datasets will be largereal-world examples an example of synthetic two-class classification dataset is the forge datasetwhich has two features the following code creates scatter plot (figure - visualizing all of the data points in this dataset the plot has the first feature on the -axis and the second feature on the -axis as is always the case in scatter plotseach data point is represented as one dot the color and shape of the dot indicates its classin[ ]generate dataset xy mglearn datasets make_forge(plot dataset mglearn discrete_scatter( [: ] [: ]yplt legend(["class ""class "]loc= plt xlabel("first feature"plt ylabel("second feature"print(" shape{}format( shape)out[ ] shape( discussing all of them is beyond the scope of the bookand we refer you to the scikit-learn documentation for more details supervised learning