supervised machine learning when reading from file all of the values are strings this next method tries to convert each values into number (an int or floator booleanif it is possible learnproblem py -(continued def interpret_elements(str_list)"""make the elements of string list str_list numerical if possible otherwise remove initial and trailing spaces ""res [for in str_listtryres append(int( )except valueerrortryres append(float( )except valueerrorse strip(if se in ["true","true","true"]res append[trueif se in ["false","false","false"]res append[falseelseres append( strip()return res augmented features sometimes we want to augment the features with new features computed from the old features (eg the product of featureshere we allow the creation of new dataset from an old dataset but with new features note that special cases of these are kernelsmapping the original feature space into new spacewhich allow neat way to do learning in the augmented space for many mappings (the "kernel trick"this is beyond the scope of aipythonthose interested should read about support vector machines feature is function of examples unary feature constructor takes feature and returns new feature binary feature combiner takes two features and returns new feature learnproblem py -(continued class data_set_augmented(data_set)def __init__(selfdatasetunary_functions=[]binary_functions=[]include_orig=true)"""creates dataset like dataset but with new features unary_function is list of unary feature constructors binary_functions is list of binary feature combiners include_orig specifies whether the original features should be included version june