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Tool Supported data models (conceptual, logical, physical) Supported notations Forward engineering Reverse engineering Model/database comparison and synchronization Teamwork/repository Database Workbench: Conceptual, logical, physical IE (Crow’s foot) Yes Yes Update database and/or update model No Enterprise Architect
Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. [1] In the context of machine learning and more generally statistical analysis , this may be the selection of a statistical model from a set of candidate models, given data.
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]
abess (Adaptive Best Subset Selection, also ABESS) is a machine learning method designed to address the problem of best subset selection.It aims to determine which features or variables are crucial for optimal model performance when provided with a dataset and a prediction task.
The Comparison of database administration tools article contains information about "Visual schema/model/E-R diagram design" which is part of data modeling. Pages in category "Data modeling tools" The following 16 pages are in this category, out of 16 total.
More generally, we might want to compare a model of the data with a model of transformed data. Following is an illustration of how to deal with data transforms (adapted from Burnham & Anderson (2002, §2.11.3): "Investigators should be sure that all hypotheses are modeled using the same response variable").
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