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Machine learningand data mining. Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step.
Introduction. In general, a neural model can be characterized by 4 parameters: size of the model, size of the training dataset, cost of training, performance after training. Each of these four variables can be precisely defined into a real number, and they are empirically found to be related by simple statistical laws, called "scaling laws ...
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes. The method was invented by John Platt in the context of support vector machines , [1] replacing an earlier method by Vapnik , but can be applied to other classification models. [2]
In machine learning, a hyperparameter is a parameter, such as the learning rate or choice of optimizer, which specifies details of the learning process, hence the name hyper parameter. This is in contrast to parameters which determine the model itself. Hyperparameters can be classified as model hyperparameters, that typically cannot be inferred ...
Tensor (machine learning) Tensor informally refers in machine learning to two different concepts that organize and represent data. Data may be organized in a multidimensional array ( M -way array) that is informally referred to as a "data tensor"; however in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain ...
Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals rather than the typical residuals used in traditional boosting. It gives a prediction model in the form of an ensemble of weak prediction models, i.e., models that make very few assumptions about the data, which are ...
Machine learningand data mining. Feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Stylometry and DNA microarray analysis are two cases where feature selection is used. It should be distinguished from feature extraction. [1]
e. Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...