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Users can download the raw PAE data for all residue pairs in a custom JSON format for further analysis or visualization using a programming language such as Python. The format of the JSON file is as follows:
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement. The out-of-bag set is all data not chosen in the sampling process ...
Data type validation is customarily carried out on one or more simple data fields. The simplest kind of data type validation verifies that the individual characters provided through user input are consistent with the expected characters of one or more known primitive data types as defined in a programming language or data storage and retrieval ...
The specificity of the test is equal to 1 minus the false positive rate. In statistical hypothesis testing, this fraction is given the Greek letter α, and 1 − α is defined as the specificity of the test. Increasing the specificity of the test lowers the probability of type I errors, but may raise the probability of type II errors (false ...
Therefore, HTTP/1.1 added status codes 303 and 307 to distinguish between the two behaviours. [1]: §15.4 303 See Other (since HTTP/1.1) The response to the request can be found under another URI using the GET method. When received in response to a POST (or PUT/DELETE), the client should presume that the server has received the data and should ...
varied by: 1) provision of general calorie recommendations, 2) provision of specific calorie information, and 3) whether high- or low-calorie items were more easily accessible. Results suggest that a strictly informational approach may be less effective than subtle guidance in enticing fast-food customers towards healthier meals.
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.