Final answer:
Cross-validation in statistics provides an estimate of a predictive model's performance and includes calculations of performance statistics such as mean and variance, using methods that involve inferential statistics, sampling variability, and confidence intervals.
Step-by-step explanation:
Cross-validation provides not only a simple estimate of the performance of a predictive model, but also some statistics on the estimated performance (mean, variance, etc.). In statistics, we use sample data to make generalizations about an unknown population through inferential statistics, which helps us to estimate population parameters. The sample mean (x) is the point estimate for the population mean (μ), while the sample standard deviation (s) is the point estimate for the population standard deviation (σ).
When constructing confidence intervals, we consider the sampling variability of a statistic, which is typically measured by its standard error. The confidence interval encompasses a range of values that, with a certain level of confidence, includes the unknown population parameter. For instance, a 95% confidence interval indCross-validation provides not only a simple estimate of population parameter, but also some statistics on the estimated performance (mean, variance,...).
Confidence intervals can be used to estimate the population standard deviation using sample data. The sample mean, x, is the point estimate for the population mean, μ. The sample standard deviation, s, is the point estimate for the population standard deviation, o.The margin of error in a confidence interval depends on the confidence level and the standard error of the meanicates that we expect that percentage of all calculated confidence intervals to contain the true population meanIn the context of cross-validation, which is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set, the mean and variance of the performance metrics across the different folds provide an indication of the model's robustness and its potential variability in performance.