Nd bias bootstrap #374
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Nd bias bootstrap #374
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lucidtronix
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Jul 29, 2020
| for performance, group_name in zip(performances, group_names): | ||
| performance_dict[group_col] += [group_name] * len(performance) | ||
| performance_dict[metric_name] += performance | ||
| sns.boxplot(x=group_col, y=metric_name, data=pd.DataFrame(performance_dict), ax=ax) |
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Do you think we should also compute P-Values for the groups belonging to different distributions given these performances? (Can wait for another PR)
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I think that should be another PR because it's not obvious to me how to calculate the p-value
lucidtronix
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Jan 13, 2023
Adds protected tensor maps gathered during test set inference Adds bootstrapped performance comparison per class of protected tensor maps
lucidtronix
pushed a commit
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Jan 13, 2023
Adds protected tensor maps gathered during test set inference Adds bootstrapped performance comparison per class of protected tensor maps
lucidtronix
pushed a commit
that referenced
this pull request
Jan 13, 2023
Adds protected tensor maps gathered during test set inference Adds bootstrapped performance comparison per class of protected tensor maps
lucidtronix
pushed a commit
that referenced
this pull request
Jan 13, 2023
Adds protected tensor maps gathered during test set inference Adds bootstrapped performance comparison per class of protected tensor maps
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This builds on
gn_sf_biasby replacing the scatter blots with bootstrapped performance by protected class. Here is an example running test scalar with protected tmaps (on an untrained model)