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Detailed results of leave-one-out cross-validation in R format

LPSBest computes the similarity based on a preset segment length factor and depth. The setting of segment length and the depth is determined based on a leave-one-out (LOO) cross-validation (CV) accuracy on the training data. Attached zip file contains 75 *.Rdata files that contains a list called 'tuned'. 'tuned' has the following information (recall that our experiments allowed for 21 model evaluations).

  • $ params: Evaluated parameters. Segment length setting as the factor of time series length and depth  (a 21 by 2 matrix)  
  • $ errors: A 21 by number of training time series matrix where ijth entry is 0 if jth time series is classified correctly by the parameter combination in row i of $params, 1 otherwise.
  • $ best.error  : best LOO CV error rate
  • $ best.seg    : segment length factor that provides the best LOO CV
  • $ best.depth  : depth that provides the best LOO CV
 
Similar information is provided in the package's manual on CRAN (page 14). Function 'tuneLearnPattern'  returns:
 
A list with the following components:
  • params evaluated parameter combinations as a matrix where rows are parameter combinations and columns represent the settings. First and seconds columns are the evaluated segment length level and depth respectively.
  • errors cross-validation error rate for each parameter combinations
  • best.error the minimum cross-validation error rate obtained.
  • best.seg the segment length level that provides the minimum cross-validation error.
  • best.depth the depth level that provides the minimum cross-validation error.
  • random.split split type used for learning patterns.



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Created 2014-11-19 20:06:39
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Copyright © 2014 mustafa gokce baydogan

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