supervised machine learning dataset spect iris carbool holiday mail reading simp regr examples #columns input types boolean real categorical/real boolean boolean numerical target type boolean categorical real boolean boolean numerical figure some of the datasets used here mlr is uci machine learning repository representations of data and predictions the code uses the following definitions and conventionsa data set is an enumeration of examples an example is list (or tupleof values the values can be numbers or strings feature is function from examples into the range of the feature each feature also has the following attributesf ftype the type of fone of"boolean""categorical""numericf frange the range of frepresented as list __doc__ the docstringa string description of (for printingthus for examplea boolean feature is function from the examples into {falsetruesoif is boolean featuref frange =[falsetrue]and if is an examplef (eis either true or false learnproblem py - learning problem import mathrandomstatistics import csv from display import displayable from utilities import argmax boolean [falsetruewhen creating data setwe partition the data into training set (trainand test set (testthe target feature is the feature that we are making prediction of dataset ds has the following attributes ds train list of the training examples ds test list of the test examples ds target_index the index of the target version june