quick look at machine learning unsupervised learningthe problem is harder typicallywe choose features based upon our intuition about which features might be relevant to the kinds of structure we would like to find consider figure which contains table of feature vectors and the label (reptile or notwith which each vector is associated name egglaying scales poisonous coldblooded legs reptile cobra true true true true yes rattlesnake true true true true yes boa constrictor false true false true yes alligator true true false true yes dart frog true false true false no salmon true true false true no python true true false true yes figure namefeatures and labels for assorted animals supervised machine learning algorithm (or humangiven only the information about cobras cannot do much more than to remember the fact that cobra is reptile nowlet' add the information about rattlesnakes we can begin to generalizeand might infer the rule that an animal is reptile if it lays eggshas scalesis poisonousis cold-bloodedand has no legs nowsuppose we are asked to decide if boa constrictor is reptile we might answer "no,because boa constrictor is neither poisonous nor egg-laying but this would be the wrong answer of courseit is hardly surprising that attempting to generalize from two examples might lead us astray once we include the boa constrictor in our training datawe might formulate the new rule that an animal is reptile if it is has scalesis cold-bloodedand is legless in doing sowe are discarding the features egg-laying and poisonous as irrelevant to the classification problem if we use the new rule to classify the alligatorwe conclude incorrectly that since it has legs it is not reptile once we include the alligator in the training data we reformulate the rule to allow reptiles to have either none or four legs when we look at the dart frogwe correctly conclude that it is not reptilesince it is not cold-blooded howeverwhen we use our current rule to classify the salmonwe incorrectly conclude that salmon is reptile we can add yet more complexity to our ruleto separate salmon from alligatorsbut it' losing battle there is no way to modify our rule so that it will correctly classify both salmon and pythons--since the feature vectors of these two species are identical this kind of problem is more common than not in machine learning it is quite rare to have feature vectors that contain enough information to classify things perfectly in this casethe problem is that we don' have enough features if we