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write the select train function described in section 152 this function has 3 parameters angle new obs the angle of the
which statement about k-fold cross-validation is falsea the cross-validation process is repeated k timesb all
write the read data function described in section 134 the arguments to this function are the file name filename and
in section 25 we created an id for a runner by pasting together name year of birth and state consider using the home
further refine the set of athletes in the longitudinal analysis by dropping those ids see section 25 who have a large
consider adapting a non-parametric curve fitting approach to the longitudinal analysis rice 4 suggests modeling an
in section 27 we discovered that the html file for the male 2000 results was so poorly formatted that html parse was
revise the extract res table function in section 27 so that it takes an additional parameter file give the file
revise the extract res table function in section 27 so that it can read the male 2009 results carefully examine the
write the code to read the raw training data into the data structure in the first approach described in section 12 that
compare the total time it takes to read the raw data and create the data frame for the two approaches described in
write the convert time function described in section 23 this function takes a string where time is in either the
follow the approach developed in section 22 to read the files for the female runners and then process them using the
normalize each male runners time by the fastest time for the runner of the same age to do this find the fastest runner
follow the procedures developed in section 25 to clean the female runners names and hometowns and create longitudinal
compare the size of two data structures the data frame created in section 12 and the data frame created in the previous
examine the time variable in the offline data any change over time in the characteristics of the signal caused by eg
we have seen that the 1999 runners were typically older than the 2012 runners compare the age distribution of the
modify the piecewise linear fit from section 242 to include a hing at 70 examine the coefficients from the fit and
consider the treatment of urls in the text cleaning in find msg words of section 353 notice that this function often
in section 381 we used the read dcf function to read the key value data in the email headers in this exercise we use
write code to handle the attachments in the message separately from the text in the body of the message since each
consider the other parameters that can be used to control the recursive partitioning process read the documentation for
in section 363 we used the test set that we had put aside to both select tau the threshold for the log odds and to