Can any one aid be by giving details about how JACKKNIFE test is performed for a machine learning algorithm...Thanks in advance!
Jackknifing, which is similar to bootstrapping, is used in statistical inference to estimate the bias and standard error in a statistic, when a random sample of observations is used to calculate it. The basic idea behind the jackknife estimator lies in systematically recomputing the statistic estimate leaving out one observation at a time from the sample set. From this new set of "observations" for the statistic an estimate for the bias can be calculated, as well as an estimate for the variance of the statistic.
Both methods estimate the variability of a statistic from the variability of that statistic between subsamples, rather than from parametric assumptions. The jackknife is a less general technique than the bootstrap, and explores the sample variation differently. However the jackknife is easier to apply to complex sampling schemes, such as multi-stage sampling with varying sampling weights.
Bootstrap however will yield slightly different results when repeated on the same data, whereas the jackknife gives exactly the same result each time.
I have attached a paper entitles "Resampling Methods: Randomization Tests, Jackknife and Bootstrap Estimators" that has more information if you are interested.
Jackknifing, which is similar to bootstrapping, is used in statistical inference to estimate the bias and standard error in a statistic, when a random sample of observations is used to calculate it. The basic idea behind the jackknife estimator lies in systematically recomputing the statistic estimate leaving out one observation at a time from the sample set. From this new set of "observations" for the statistic an estimate for the bias can be calculated, as well as an estimate for the variance of the statistic.
Both methods estimate the variability of a statistic from the variability of that statistic between subsamples, rather than from parametric assumptions. The jackknife is a less general technique than the bootstrap, and explores the sample variation differently. However the jackknife is easier to apply to complex sampling schemes, such as multi-stage sampling with varying sampling weights.
Bootstrap however will yield slightly different results when repeated on the same data, whereas the jackknife gives exactly the same result each time.
I have attached a paper entitles "Resampling Methods: Randomization Tests, Jackknife and Bootstrap Estimators" that has more information if you are interested.
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