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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]
Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
Both of these can introduce systematic differences between the training and validation sets. For example, if a model for prediction of trend changes in financial quotations is trained on data for a certain five-year period, it is unrealistic to treat the subsequent five-year period as a draw from the same population.
More abstractly, learning curves plot the difference between learning effort and predictive performance, where "learning effort" usually means the number of training samples, and "predictive performance" means accuracy on testing samples. [3] Learning curves have many useful purposes in ML, including: [4] [5] [6] choosing model parameters ...
Salesforce, Inc. is an American cloud-based software company headquartered in San Francisco, California.It provides applications focused on sales, customer service, marketing automation, e-commerce, analytics, artificial intelligence, and application development.
Software validation checks that the software product satisfies or fits the intended use (high-level checking), i.e., the software meets the user requirements, not as specification artifacts or as needs of those who will operate the software only; but, as the needs of all the stakeholders (such as users, operators, administrators, managers ...
The technique uses hypothesis testing to accept a model if the difference between a model's variable of interest and a system's variable of interest is within a specified range of accuracy. [7] A requirement is that both the system data and model data be approximately Normally Independent and Identically Distributed (NIID) .
Performance-wise, unusually high accuracy or significant discrepancies between training and test results often indicate leakage. [6] Inconsistent cross-validation outcomes may also signal issues. Feature examination involves scrutinizing feature importance rankings and ensuring temporal integrity in time series data.