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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).
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]
ANNs have been used to diagnose several types of cancers [184] [185] and to distinguish highly invasive cancer cell lines from less invasive lines using only cell shape information. [ 186 ] [ 187 ] ANNs have been used to accelerate reliability analysis of infrastructures subject to natural disasters [ 188 ] [ 189 ] and to predict foundation ...
For the reduction cell, the initial operation applied to the cell's inputs uses a stride of two (to reduce the height and width). [4] The learned aspect of the design included elements such as which lower layer(s) each higher layer took as input, the transformations applied at that layer and to merge multiple outputs at each layer.
Dataset to predict the number of comments a post will receive based on features of that post. Many features of each post extracted. 60,021 Text Regression 2014 [102] [103] K. Buza PubMed Central: PubMed® comprises more than 35 million citations for biomedical literature from MEDLINE, life science journals, and online books. None 35 Million ...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
The positive prediction value answers the question "If the test result is positive, how well does that predict an actual presence of disease?". It is calculated as TP/(TP + FP); that is, it is the proportion of true positives out of all positive results. The negative prediction value is the same, but for negatives, naturally.
As the Transformer architecture natively processes numerical data, not text, there must be a translation between text and tokens. A token is an integer that represents a character, or a short segment of characters. On the input side, the input text is parsed into a token sequence.