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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]
In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and usually a validation set) changes with the number of training iterations (epochs) or the amount of training data. [1]
If the mean of the model is μ m and the mean of system is μ s then the difference between the model and the system is D = μ m - μ s. The hypothesis to be tested is if D is within the acceptable range of accuracy. Let L = the lower limit for accuracy and U = upper limit for accuracy. Then H 0 L ≤ D ≤ U. versus H 1 D < L or D > U. is to ...
This method, also known as Monte Carlo cross-validation, [21] [22] creates multiple random splits of the dataset into training and validation data. [23] For each such split, the model is fit to the training data, and predictive accuracy is assessed using the validation data. The results are then averaged over the splits.
In statistics, model validation is the task of evaluating whether a chosen statistical model is appropriate or not. Oftentimes in statistical inference, inferences from models that appear to fit their data may be flukes, resulting in a misunderstanding by researchers of the actual relevance of their model.
Validity is the main extent to which a concept, conclusion, or measurement is well-founded and likely corresponds accurately to the real world. [1] [2] The word "valid" is derived from the Latin validus, meaning strong.
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.
There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Calibration can mean a reverse process to regression, where instead of a future dependent variable being predicted from known explanatory variables, a known observation of the dependent variables is used to predict a corresponding explanatory variable; [1]