Experimental

Random Forest regression for continuous targets (e.g., biomass, moisture, temperature) from raster predictors.

remote_sensing regression random_forest legacy-port

## Parameters

Name | Description | Required | Default
--- | --- | --- | ---
`inputs` | Array of single-band input rasters. | Required | `['band1.tif', 'band2.tif', 'band3.tif']`
`training_data` | Point vector training data path. | Required | `training_points.shp`
`field` | Numeric target field in training_data attributes. | Required | `value`
`scaling` | Feature scaling mode: none (default), normalize, standardize. | Optional | `none`
`n_trees` | Number of trees in the forest (default 200). | Optional | `200`
`min_samples_leaf` | Minimum number of samples required at a leaf node (default 1). | Optional | `1`
`min_samples_split` | Minimum number of samples required to split an internal node (default 2). | Optional | `2`
`output` | Optional output raster path. | Optional | —

## Examples

Run random forest regression on multiband predictors.

    wbe.random_forest_regression(field='target', inputs=['band1.tif', 'band2.tif', 'band3.tif'], n_trees=300, output='rf_regression.tif', scaling='standardize', training_data='training_points.shp')

## Project Links

WbW Homepage User Manual Support WbW