Histograms are an example of data binning used in order to observe underlying
frequency distributions. They typically occur in
one-dimensional space and in
equal intervals for ease of visualization. Data binning may be used when small instrumental shifts in the spectral dimension from
mass spectrometry (MS) or
nuclear magnetic resonance (NMR) experiments will be falsely interpreted as representing different components, when a collection of data profiles is subjected to
pattern recognition 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
NMR the
chemical shift axis may be discretized and coarsely binned, and in
MS the spectral accuracies may be rounded to an integer multiple of the
dalton. Also, several
digital camera systems incorporate an automatic pixel binning function to improve image contrast. Binning is also used in
machine learning to speed up the decision-tree
boosting method for supervised classification and regression in algorithms such as
Microsoft's
LightGBM and
scikit-learn's Histogram-based Gradient Boosting Classification Tree. ==See also==