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  2. Quantification (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Quantification_(machine...

    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.

  3. Approximate entropy - Wikipedia

    en.wikipedia.org/wiki/Approximate_entropy

    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.

  4. ID3 algorithm - Wikipedia

    en.wikipedia.org/wiki/ID3_algorithm

    ID3 is harder to use on continuous data than on factored data (factored data has a discrete number of possible values, thus reducing the possible branch points). If the values of any given attribute are continuous , then there are many more places to split the data on this attribute, and searching for the best value to split by can be time ...

  5. Conformal prediction - Wikipedia

    en.wikipedia.org/wiki/Conformal_prediction

    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]

  6. Sample entropy - Wikipedia

    en.wikipedia.org/wiki/Sample_entropy

    Like approximate entropy (ApEn), Sample entropy (SampEn) is a measure of complexity. [1] But it does not include self-similar patterns as ApEn does. For a given embedding dimension, tolerance and number of data points, SampEn is the negative natural logarithm of the probability that if two sets of simultaneous data points of length have distance < then two sets of simultaneous data points of ...

  7. Learning curve (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Learning_curve_(machine...

    Download QR code; Print/export Download as PDF; ... Learning curves can also be tools for determining how much a model benefits from adding more training data, ...

  8. Uncertainty quantification - Wikipedia

    en.wikipedia.org/wiki/Uncertainty_quantification

    An example of a source of this uncertainty would be the drag in an experiment designed to measure the acceleration of gravity near the earth's surface. The commonly used gravitational acceleration of 9.8 m/s² ignores the effects of air resistance, but the air resistance for the object could be measured and incorporated into the experiment to ...

  9. Raven's Progressive Matrices - Wikipedia

    en.wikipedia.org/wiki/Raven's_Progressive_Matrices

    The cover of a test booklet for Raven's Standard Progressive Matrices. Raven's Progressive Matrices (often referred to simply as Raven's Matrices) or RPM is a non-verbal test typically used to measure general human intelligence and abstract reasoning and is regarded as a non-verbal estimate of fluid intelligence. [1]