User:Closed Limelike Curves/Data binning

In statistics and machine learning, binning, bucketing, or discretization is the practice of transforming a continuous variable into a discrete one by combining similar observations into one category.. For example, a researcher may combine the ages of several participants in a study into a handful number of age intervals (e.g. grouping participants from ages of five years together).

In big data processing, discretization is used to speed up algorithms that would otherwise be impossible to compute, reducing the size of the dataset (and thus processing time) by combining several similar observations. However, cargo cult , it is misuse of statistics s (statistics performed by non-statisticians), they are commonly misused . Whenever continuous data is discretized

Examples of correct use

Histograms are an example of data binning used in order to observe underlying frequency distributions, and can be used to quickly give a "rough idea" of how a dataset is spaced along a line. However, when trying to , the histogram can be replaced with its more-precise continuous counterpart, called kernel density estimation. This generally provides a substantial improvement in accuracy, but can be less intuitive or .

Binning is sometimes 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.

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 integer atomic mass unit values. Also, several digital camera systems incorporate an automatic pixel binning function to improve image contrast.

In statistics and machine learning, discretization refers to the process of converting or partitioning continuous attributes, features or variables to discretized or nominal attributes/features/variables/intervals. This can be useful when creating probability mass functions – formally, in density estimation. It is a form of discretization in general and also of binning, as in making a histogram.

Typically data is discretized into partitions of K equal lengths/width (equal intervals) or K% of the total data (equal frequencies).

Mechanisms for discretizing continuous data include Fayyad & Irani's MDL method, which uses mutual information to recursively define the best bins, CAIM, CACC, Ameva, and many others

Many machine learning algorithms are known to produce better models by discretizing continuous attributes.

Software

This is a partial list of software that implement MDL algorithm.

• discretize4crf tool designed to work with popular CRF implementations (C++)
• mdlp in the R package discretization
• Discretize in the R package RWeka

See also

• Density estimation
• Continuity correction

See also

• Binning (disambiguation)
• Censoring (statistics)
• Discretization of continuous features
• Grouped data
• Histogram
• Level of measurement
• Quantization (signal processing)
• Rounding