features and feature engineering one interesting aspect of these features is that the compactness feature is not actually new measurementbut function of the previous two featuresarea and perimeter it is often very useful to derive new combined features this is general area normally termed feature engineeringit is sometimes seen as less glamorous than algorithmsbut it may matter more for performance ( simple algorithm on well-chosen features will perform better than fancy algorithm on not-so-good featuresin this casethe original researchers computed the "compactness"which is typical feature for shapes (also called "roundness"this feature will have the same value for two kernelsone of which is twice as big as the other onebut with the same shape howeverit will have different values for kernels that are very round (when the feature is close to oneas compared to kernels that are elongated (when the feature is close to zerothe goals of good feature are to simultaneously vary with what matters and be invariant with what does not for examplecompactness does not vary with size but varies with the shape in practiceit might be hard to achieve both objectives perfectlybut we want to approximate this ideal you will need to use background knowledge to intuit which will be good features fortunatelyfor many problem domainsthere is already vast literature of possible features and feature types that you can build upon for imagesall of the previously mentioned features are typicaland computer vision libraries will compute them for you in text-based problems toothere are standard solutions that you can mix and match (we will also see this in later often thoughyou can use your knowledge of the specific problem to design specific feature even before you have datayou must decide which data is worthwhile to collect thenyou need to hand all your features to the machine to evaluate and compute the best classifier natural question is whether or not we can select good features automatically this problem is known as feature selection there are many methods that have been proposed for this problembut in practicevery simple ideas work best it does not make sense to use feature selection in these small problemsbut if you had thousands of featuresthrowing out most of them might make the rest of the process much faster