SAP HANA Predictive Analysis Library (PAL)

3.6 Preprocessing Algorithms
The records in business database are usually not directly ready for predictive analysis due to the following
reasons:
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Some data come in large amount, which may exceed the capacity of an algorithm.
Some data contains noisy observations which may hurt the accuracy of an algorithm.
Some attributes are badly scaled, which can make an algorithm unstable.
To address the above challenges, PAL provides several convenient algorithms for data preprocessing.
3.6.1 Binning
Binning data is a common requirement prior to running certain predictive algorithms. It generally reduces the
complexity of the model, for example, the model in a decision tree.
Binning methods replace a value by a "bin number" defined by all elements of its neighborhood, that is, the bin
it belongs to. The ordered values are distributed into a number of bins. Because binning methods consult the
neighborhood of values, they perform local smoothing.
Note
Binning can only be used on a table with only one attribute.
Binning Methods
There are four binning methods:
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Equal widths based on the number of bins
Specify an integer to determine the number of equal width bins and calculate the range values by:
BandWidth = (MaxValue - MinValue) / K
Where MaxValue is the biggest value of every column, MinValue is the smallest value of every column,
and K is the number of bins.
For example, according to this rule:
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MinValue + BinWidth > Values in Bin 1 ≥ MinValue
MinValue + 2 * BinWidth > Values in Bin 2 ≥ MinValue + BinWidth
Equal bin widths defined as a parameter
Specify the bin width and calculate the start and end of bin intervals by:
Start of bin intervals = Minimum data value – 0.5 * Bin width
End of bin intervals = Maximum data value + 0.5 * Bin width
For example, assuming the data has a range from 6 to 38 and the bin width is 10:
Start of bin intervals = 6 – 0.5 * 10 = 1
End of bin intervals = 38 + 0.5 * 10 = 43
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 431