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A fitness function does not necessarily have to be able to calculate an absolute value, as it is sometimes sufficient to compare candidates in order to select the better one. A relative indication of fitness (candidate a is better than b) is sufficient in some cases, [5] such as tournament selection or Pareto optimization.
In fitness proportionate selection, as in all selection methods, the fitness function assigns a fitness to possible solutions or chromosomes.This fitness level is used to associate a probability of selection with each individual chromosome.
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]
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.
In practice, is usually chosen between 0.98 and 1. [1] By using type-II maximum likelihood estimation the optimal λ {\displaystyle \lambda } can be estimated from a set of data. [ 2 ]
CLPython is an implementation of the Python programming language written in Common Lisp. This project allow to call Lisp functions from Python and Python functions from Lisp. Licensed under LGPL. CLPython was started in 2006, but as of 2013, it was not actively developed and the mailing list was closed. [1]
In machine learning (ML), boosting is an ensemble metaheuristic for primarily reducing bias (as opposed to variance). [1] It can also improve the stability and accuracy of ML classification and regression algorithms. Hence, it is prevalent in supervised learning for converting weak learners to strong learners. [2]
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.