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is a way to group numbers of more-or-less continuous values into a smaller number of "bins". For example, if you have data about a group of people, you might want to arrange their ages into a smaller number of age intervals (for example, grouping every five years together). It can also be used in
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analysis. A straightforward way to cope with this problem is by using binning techniques in which the spectrum is reduced in resolution to a sufficient degree to ensure that a given peak remains in its bin despite small spectral shifts between analyses. For example, in
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throughout an image, by summing or averaging their values, during or after readout. It reduces the amount of data; also the relative noise level in the result is lower.
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Data binning may be used when small instrumental shifts in the spectral dimension from
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systems incorporate an automatic pixel binning function to improve image contrast.
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method for supervised classification and regression in algorithms such as
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are an example of data binning used in order to observe underlying
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273:"LightGBM: A Highly Efficient Gradient Boosting Decision Tree"
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Histogram-based
Gradient Boosting Classification Tree
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axis may be discretized and coarsely binned, and in
152:the spectral accuracies may be rounded to integer
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275:. Neural Information Processing Systems (NIPS)
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207:Discretization of continuous features
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227:Quantization (signal processing)
252:"Use of binning in photography"
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133:nuclear magnetic resonance
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163:Binning is also used in
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76:Statistical data binning
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111:frequency distributions
81:multivariate statistics
306:-related article is a
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156:values. Also, several
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222:Level of measurement
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351:Categories
304:statistics
279:2019-12-18
258:2011-01-18
238:References
107:Histograms
217:Histogram
173:Microsoft
122:intervals
232:Rounding
191:See also
177:LightGBM
169:boosting
131:(MS) or
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