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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]
For example, by only doing pull-ups on the same regular pull-up bar, the body becomes adapted to this specific physical demand, but not necessarily to other climbing patterns or environments. In 1958, Berkeley Professor of Physical Education Franklin M. Henry proposed the "Specificity Hypothesis of Motor Learning". [citation needed]
The encoding specificity principle is the general principle that matching the encoding contexts of information at recall assists in the retrieval of episodic memories. It provides a framework for understanding how the conditions present while encoding information relate to memory and recall of that information.
Active learning: Instead of assuming that all of the training examples are given at the start, active learning algorithms interactively collect new examples, typically by making queries to a human user. Often, the queries are based on unlabeled data, which is a scenario that combines semi-supervised learning with active learning.
Specificity (true negative rate) is the probability of a negative test result, conditioned on the individual truly being negative. If the true status of the condition cannot be known, sensitivity and specificity can be defined relative to a " gold standard test " which is assumed correct.
Highly abstract or novel new concepts can be difficult to understand without concrete examples. [citation needed] Specification by example is intended to construct an accurate understanding, and significantly reduces feedback loops in software development, leading to less rework, higher product quality, faster turnaround time for software changes and better alignment of activities of various ...
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
The relationship between sensitivity and specificity, as well as the performance of the classifier, can be visualized and studied using the Receiver Operating Characteristic (ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both (such as in the red/blue ball example given above).