Note
This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the What you really need to know section for the big picture.
julearn.stats.corrected_ttest#
- julearn.stats.corrected_ttest(*scores, df=None, method='bonferroni', alternative='two-sided')#
Perform corrected t-test on the scores of two or more models.
- Parameters:
- *scorespd.DataFrame
DataFrames containing the scores of the models. The DataFrames must be the output of run_cross_validation
- df: int
Degrees of freedom.
- methodstr
Method used for testing and adjustment of pvalues. Can be either the full name or initial letters. Available methods are:
bonferroni : one-step correction
sidak : one-step correction
holm-sidak : step down method using Sidak adjustments
holm : step-down method using Bonferroni adjustments
simes-hochberg : step-up method (independent)
hommel : closed method based on Simes tests (non-negative)
fdr_bh : Benjamini/Hochberg (non-negative)
fdr_by : Benjamini/Yekutieli (negative)
fdr_tsbh : two stage fdr correction (non-negative)
fdr_tsbky : two stage fdr correction (non-negative)
- alternative{‘two-sided’, ‘less’, ‘greater’}, optional
Defines the alternative hypothesis. The following options are available (default is ‘two-sided’):
‘two-sided’: the means of the distributions underlying the samples are unequal.
‘less’: the mean of the distribution underlying the first sample is less than the mean of the distribution underlying the second sample.
‘greater’: the mean of the distribution underlying the first sample is greater than the mean of the distribution underlying the second sample.
