in generalwe will call any measurement from our data as features additionallyfor each plantthe species was recorded the question now isif we saw new flower out in the fieldcould we make good prediction about its species from its measurementsthis is the supervised learning or classification problemgiven labeled exampleswe can design rule that will eventually be applied to other examples this is the same setting that is used for spam classificationgiven the examples of spam and ham (non-spam -mailthat the user gave the systemcan we determine whether newincoming message is spam or notfor the momentthe iris dataset serves our purposes well it is small ( examples features eachand can easily be visualized and manipulated the first step is visualization because this dataset is so smallwe can easily plot all of the points and all twodimensional projections on page we will thus build intuitions that can then be extended to datasets with many more dimensions and datapoints each subplot in the following screenshot shows all the points projected into two of the dimensions the outlying group (trianglesare the iris setosa plantswhile iris versicolor plants are in the center (circleand iris virginica are indicated with "xmarks we can see that there are two large groupsone is of iris setosa and another is mixture of iris versicolor and iris virginica