# forest.regress

## forest.regress(..) 🡒 U.*, function

Gives access to a random forest regression algorithm. A training dataset (Example) and an evaluation dataset (Sample) must be provided. These two tables contain the same series of column attributes, for a different set of Id. In addition, the Example table contains a further column called Label, which represents the desired output of the regression function. The Example table shall thus be conceived as a Sample with a Label.

The function syntax is the following:

Sample.Label = forest.regress(
training: Example.A, Example.B, Example.C // number/boolean/text
trainingBow : Example.D // plain text, optional
label: Example.Label // number
evaluation: Sample.A, Sample.B, Sample.C // number/boolean/text
evaluationBow: Sample.D) // plain text, optional


This call-function returns for each Id a ranvar (probability distribution) representing the quantity Label. Up to 16 different attributes are supported as training and evaluation entries. Of these, 8 at most can be number vectors and 8 at most can be Boolean or text vectors representing categories. Optionally, a text vector of words can be provided as trainingBow and evaluationBow: here the text string is treated as a bag-of-words, and analysed in terms of words occurrences.