Random subsampling

Random subsampling. The data are split by random selection of data points without replacement into training and validation samples. The model is then fitted to the training sample and the performance of the model is evaluated on the validation sample. This is repeated a number of times, and the total performance is found by averaging the performance of all iterations. This process is. similar to the hold-out method and is also referred to as repeated hold-out. A drawback of this method is that different validation sets may overlap. 

In order to capture as much of the potential variation as possible, the samples analysed included Pj elements of 0. excavata from most of its spatiotemporal range (see Table 14.1). Assignment of these elements to 0. excavata was based on published opinions and the active input of conodont workers with experience in this taxon. The full data set is provided in Appendix 5. Original sample sizes ranged between 9 and 44 elements. Inequality in sample size can mask differences in variance between samples, so samples were reduced through random subsampling to produce sample sizes of between 10 and 20 (except for the poorly preserved American topotype material). 

Within regions, are endemic taxa more or less disparate than a random subsample of all taxa known from within the region during that stage ... 

The objective of sediment and water sampling is to obtain reliable information about the behavior of agrochemicals applied to paddy fields. Errors or variability of results can occur randomly or be due to bias. The two major sources of variability are sediment body or water body variability and measurement variability . For the former, a statistical approach is required the latter can be divided into sampling variability, handling, shipping and preparation variability, subsampling variability, laboratory analysis variability, and between-batch variability. ... 

First, we conjecture that each item is a separate marker of the taxon. Now there is a pool of indicators to work with. Second, we choose a random pair of indicators, for example, item 1 ( When I go shopping, I check several times to be sure I have my wallet/ purse with me ) and item 2 ( Before I leave my house, I check whether all windows are closed ), as the output indicators. Third, we sum scores on items 3 to 8, which makes a 7-point scale that ranges from 0 (none of the 6 checking behaviors are endorsed) to 6 (all of the 6 checking behaviors are endorsed) this is the input variable. Fourth, we calculate the covariance between items 1 and 2 in a subsample of individuals who scored 0 on the input variable, next we calculate the covariance for individuals who scored 1 on the input variable, and so forth. Fifth, we choose another pair of output indicators (e.g., items 1 and 3), and combine the other six items together to make a new input variable. This process is repeated 28 times until all possible pairs are drawn (1-2 and 2-1 are not considered different pairs). Next, we take 28 covariances from 0 subsamples and average them we do the same for all seven sets of numbers and plot the average covariances. SSMAXCOV plots look similar to the plots from the MAXCOV section and are interpreted the same way. 

The ferromagnetic fraction of the till samples was separated by standard methods by Overburden Drilling Management Ltd (ODM). Subsamples containing 22 to 110 randomly selected grains were prepared by ODM. To test for heterogeneity, two sets of 5 subsamples, from 2 till samples, were prepared from the 0.5-1.0 mm size fraction using a plastic riffle at Universite Laval. 

Two samples from the soil profile were randomly separated into very small subsamples (a few millimetres across) and analysed after aqua regia digestion to assess the heterogeneous nature of the Au distribution. Replicate sub-samples were analysed to assess the likelihood of finding Au in other soil particles using LA-... 

Define sample, representative sample, composite sample, selective sample, random sample, bulk sample, primary sample, secondary sample, subsample, laboratory sample, test sample. 

Let s look at this tool in detail. Material is poured across the riffler in a direction parallel to the channels. Adjacent channels slant in opposite directions. The material flows down the channels into receiving pans on each side. With roughly half the material in each pan, one pan is chosen at random for the sample. The procedure can be repeated several times to get subsamples. 

A subsample of five bundles was selected at random from each study site and was weighed green on the same day as they were bundled. These 15 subsamples were covered and transported by trailer from Arkansas to the Department of Forest Products, Mississippi State University, Starkville, MS. Bundles were distributed on a mowed lawn with 1,524 mm of clear space separating all sides of each bundle from its neighbor to simulate aging in a harvested setting as closely as possible. Bundles were uncovered and with no shade from any source, such that they were fully impacted by sun and rain that occurred during the study. 

Divide data into k subsamples. Each subsample is a simple random sample with replacement. Compare performance of k subsamples. 

In bootstrapping, we repeatedly analyze subsamples, instead of subsets of the known set. Each subsample is a random sample with replacement from the full sample (known set). Bootstrapping seems to perform better than cross-validation in many instances (Efron, 1983). 

Sy.stianaiif. sample. One of a sequence of samples (or subsamples), each taken among all possible samples by way of a selected alternating sequence with a random start it is intended to give unbiased statistical estimates. 

If a clinker sample is to be subjected to the extraction, a random sample of 1- to 2-mm crushed clinker particles, taken from the same fraction as previously prepared for polished section examination, is further crushed in a mortar and pestle until all the subsample passes a 75- j,m screen (No. 200 mesh). If acement is to be treated, a random sample of approximately 10 grams is sieved to produce the 45- to 75-pm fraction (325 to 200 mesh) particles left on the 75- j,m screen could be further crushed to pass the screen or, perhaps, studied microscopically to determine belite nest percentage. Sieving, however, is an optional step the main benefit is that it provides a uniformly sized powder promoting a relatively uniform level of focus by removing "boulders" that may interfere with examination and particle manipulation. If sieve confaminafion is a likely problem, one can use disposable nylon with the proper mesh opening. 

The multiple tube fermentation method requires the use of replicate tubes and dilutions of samples. The fermentation products of lactose include mixed acids and gas, which is usually detectable. Coliforms are reported in terms of MPN of organisms present. MPN tables are based on a random dispersion of coliforms (Poisson distribution). Failure to shake the samples very well will result in a nonrandom distribution that will underestimate the actual density of the bacteria. If testing drinking water, a single bottle containing 100 mL may be used or 5 replicate tubes with 20 mL each or 10 replicate tubes with 10 mL each. If testing nonpotable water such as salt water, brackish water, or sediments, multiples and subsamples of 10 mL (e.g., 0.1 1.0 10 mL) should be used. 

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