Available with Image Analyst license.

## Overview

The Predict Using Regression function computes a predicted raster based on raster data inputs and a regression model. The regression model is the output from the Train Random Trees Regression Model tool.

## Notes

The regression model is defined in an Esri regression definition (.ecd) file. It contains all the information for a specific dataset or a set of datasets, and the regression model, and is generated by the Train Random Trees Regression Model tool.

The input can be a single band, a multiband, or a multidimensional raster, or a list of these types. The types of the input rasters must be the same type of raster trained by the regression model.

- When input is a multiband raster, each band is treated as a predictor variable. The bands must be in the same order as the multiband input for the regression model training tool.
- When input is a multidimensional raster, each variable is treated as a predictor variable, and the variable must be single band and have a time dimension. The variable order and names must be the same as the input when the regression model was trained. The output is a multidimensional raster.
- The input can be a list of items. The number of the items and the order of the items must match the input when the regression model was trained.

## Parameters

The following table describes the parameters:

Parameter | Description |
---|---|

Raster | The raster dataset or datasets representing the predictor variables. It can be a single-band raster, multiple-band raster, multidimensional raster, mosaic dataset, or a raster collection. |

Input Definition File | The input Esri regression definition (.ecd) file that contains the statistics and information for the specific dataset, regression model, and chosen attributes. |