there are many other possible ways of turning binary method into multiclass one there is no single method that is clearly better in all cases howeverwhich one you use normally does not make much of difference to the final result most classifiers are binary systems while many real-life problems are naturally multiclass several simple protocols reduce multiclass problem to series of binary decisions and allow us to apply the binary models to our multiclass problem summary in sensethis was very theoretical as we introduced generic concepts with simple examples we went over few operations with classic dataset thisby nowis considered very small problem howeverit has the advantage that we were able to plot it out and see what we were doing in detail this is something that will be lost when we move on to problems with many dimensions and many thousands of examples the intuitions we gained here will all still be valid classification means generalizing from examples to build model (that isa rule that can automatically be applied to newunclassified objectsit is one of the fundamental tools in machine learningand we will see many more examples of this in forthcoming we also learned that the training error is misleadingover-optimistic estimate of how well the model does we mustinsteadevaluate it on testing data that was not used for training in order to not waste too many examples in testinga cross-validation schedule can get us the best of both worlds (at the cost of more computationwe also had look at the problem of feature engineering features are not something that is predefined for youbut choosing and designing features is an integral part of designing machine-learning pipeline in factit is often the area where you can get the most improvements in accuracy as better data beats fancier methods the on computer vision and text-based classification will provide examples for these specific settings in this we wrote all of our own code (except when we used numpyof coursethis will not be the case for the next few but we needed to build up intuitions on simple cases to illustrate the basic concepts the next looks at how to proceed when your data does not have predefined classes for classification