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In machine learning and data mining, quantification (variously called learning to quantify, or supervised prevalence estimation, or class prior estimation) is the task of using supervised learning in order to train models (quantifiers) that estimate the relative frequencies (also known as prevalence values) of the classes of interest in a sample of unlabelled data items.
This is a measure of how much information can be obtained about one random variable by observing another. The mutual information of X {\displaystyle X} relative to Y {\displaystyle Y} (which represents conceptually the average amount of information about X {\displaystyle X} that can be gained by observing Y {\displaystyle Y} ) is given by:
Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.
Lower computational demand. ApEn can be designed to work for small data samples (< points) and can be applied in real time. Less effect from noise. If data is noisy, the ApEn measure can be compared to the noise level in the data to determine what quality of true information may be present in the data.
Motivated by such relations, a plethora of related and competing quantities have been defined. For example, David Ellerman's analysis of a "logic of partitions" defines a competing measure in structures dual to that of subsets of a universal set. [14] Information is quantified as "dits" (distinctions), a measure on partitions.
Intelligence analysis is the application of individual and collective cognitive methods to weigh data and test hypotheses within a secret socio-cultural context. [1] The descriptions are drawn from what may only be available in the form of deliberately deceptive information; the analyst must correlate the similarities among deceptions and extract a common truth.
Instead of fitting only one model on all data, leave-one-out cross-validation is used to fit N models (on N observations) where for each model one data point is left out from the training set. The out-of-sample predicted value is calculated for the omitted observation in each case, and the PRESS statistic is calculated as the sum of the squares ...
NNI (Neural Network Intelligence) is a free and open-source AutoML toolkit developed by Microsoft. [3] [4] It is used to automate feature engineering, model compression, neural architecture search, and hyper-parameter tuning. [5] [6] The source code is licensed under MIT License and available on GitHub. [7]