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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.
The entropy rate of a data source is the average number of bits per symbol needed to encode it. Shannon's experiments with human predictors show an information rate between 0.6 and 1.3 bits per character in English; [21] the PPM compression algorithm can achieve a compression ratio of 1.5 bits per character in English text.
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.
The data has to conform to some standards, such as data being exchangeable (a slightly weaker assumption than the standard IID imposed in standard machine learning). For conformal prediction, a n% prediction region is said to be valid if the truth is in the output n% of the time. [3] The efficiency is the size of the output. For classification ...
[1] [2] This involves estimating sensitivity indices that quantify the influence of an input or group of inputs on the output. A related practice is uncertainty analysis , which has a greater focus on uncertainty quantification and propagation of uncertainty ; ideally, uncertainty and sensitivity analysis should be run in tandem.
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm . The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier .
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]