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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.
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.
A data point in the calibration set will result in an α-value for its true class; Prediction algorithm: For a test data point, generate a new α-value; Find a p-value for each class of the data point; If the p-value is greater than the significance level, include the class in the output [4]
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.
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:
The code output is only available for a given set of points, and it can be difficult to perform a sensitivity analysis on a limited set of data. We then build a statistical model ( meta-model , data-driven model ) from the available data (that we use for training) to approximate the code (the f {\displaystyle f} -function).
The Davies–Bouldin index (DBI), introduced by David L. Davies and Donald W. Bouldin in 1979, is a metric for evaluating clustering algorithms. [1] This is an internal evaluation scheme, where the validation of how well the clustering has been done is made using quantities and features inherent to the dataset.
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. [1] Choosing informative, discriminating, and independent features is crucial to produce effective algorithms for pattern recognition, classification, and regression tasks.