Comments (3)
Ok, so as I said the results in results_ucr109_mean.csv
are mean results over different resamples of the data. This is not the same as what you have done. If I understand correctly, you have used the original training/test split for each dataset.
The results in the first column of accuracy_ucr109_resamples.csv
represent the accuracy of the method on the original training/test split for each dataset. The average of these values ~0.8567. If you ran the method multiple times on this version of the datasets you would get slightly different results, but I would expect the mean accuracy value to be close to this figure.
In other words, the difference is based on the datasets the method has been trained (and tested) on, specifically, whether or not you are using the original training/test splits, or using resamples.
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Hi @abolfazlzamanii, thanks for your question.
What are you referring to specifically? Is '85%' the accuracy on a particular dataset, or mean accuracy over all datasets?
When you say 'your accuracy', what are you referring to? There are a number of results files on the github. The results in results_ucr109_mean.csv
represent mean accuracy over 30 resamples of each dataset. The accuracies on different resamples can be quite different. The results in accuracy_ucr109_resamples.csv
show the results for each resample separately. If you are just running the method on the original training/test split for each dataset, I would expect the results to match resample 0 (i.e., the first column) in accuracy_ucr109_resamples.csv
, as this corresponds to the original training/test split.
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I ran the program 5 times for all 109 datasets and got an average accuracy of 85% for all 109 datasets. Then I compared with the values presented in the file (results_ucr109_mean) that the average value of the total vote of 109 datasets was equal to 87%.
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Related Issues (20)
- Extending Documentation of minirocket multivariate HOT 1
- Minimum length time series HOT 1
- Feature Size HOT 2
- Unlabeled data HOT 2
- Can't set random_state when doing a gridsearchCV HOT 3
- Are minirocket.py and minirocket_dv.py the implementation for the same Mini-Rocket algorithm? HOT 1
- is is necessary to normalize data when use minirocket multivariable version? HOT 1
- datatype HOT 13
- some question about multivarible version. HOT 3
- if the kernel numbers is much less than dataset sample numbers
- is the code handle the padding right?
- Port to R language
- Datatype Error HOT 2
- Need example to use variable ROCKET HOT 1
- Feature Transformation HOT 13
- pls help to understand how your this repo code related to sktime.transformers.series_as_features.rocket HOT 1
- Can't reproduce the accuracy of 'EOGHorizontalSignal' dataset in UCR109 based on original 'MINIROCKET' exmples HOT 7
- padding problem
- Example of CSV file reading HOT 5
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