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The datasets are classified, based on the licenses, as Open data and Non-Open data. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are ...
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]
Decision tree learning is a method commonly used in data mining. [3] The goal is to create a model that predicts the value of a target variable based on several input variables. A decision tree is a simple representation for classifying examples.
Acceptance Sampling: Methods for acceptance sampling and a quality control setting. Audit: Statistical methods for auditing. The audit module offers planning, selection and evaluation of statistical audit samples, methods for data auditing (e.g., Benford’s law) and algorithm auditing (e.g., model fairness).
Decision trees, influence diagrams, utility functions, and other decision analysis tools and methods are taught to undergraduate students in schools of business, health economics, and public health, and are examples of operations research or management science methods. These tools are also used to predict decisions of householders in normal and ...
Random forests are much faster than decision trees because of using a smaller dataset. To recreate specific results, it is necessary to keep track of the exact random seed used to generate the bootstrap sets. This may be important when collecting data for research or within a data mining class.
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. When this process is repeated, such as when building a random forest, many bootstrap samples and OOB sets are created. The OOB sets can be aggregated into one dataset, but each ...
Decision Tree Model. In computational complexity theory, the decision tree model is the model of computation in which an algorithm can be considered to be a decision tree, i.e. a sequence of queries or tests that are done adaptively, so the outcome of previous tests can influence the tests performed next.