Integration of the OpenCV Machine Learning library for Random Forest classification of gridded features.
| Name | Type | Identifier | Description | Constraints |
Input | Features | Grid list, input | FEATURES | - | - |
Training Areas | Shapes, input | TRAIN_AREAS | - | - |
Output | Classification | Grid, output | CLASSES | - | - |
Look-up Table (*) | Table, output, optional | CLASSES_LUT | A reference list of the grid values that have been assigned to the training classes. | - |
Options | Grid System | Grid system | PARAMETERS_GRID_SYSTEM | - | - |
Normalize | Boolean | NORMALIZE | - | Default: 0 |
Update Colors from Features | Boolean, GUI | RGB_COLORS | Use the first three features in list to obtain blue, green, red components for class colour in look-up table. | Default: 0 |
Load Model | File path | MODEL_LOAD | Use a model previously stored to file. | - |
Class Identifier | Table field | TRAIN_CLASS | - | - |
Buffer Size | Floating point | TRAIN_BUFFER | For non-polygon type training areas, creates a buffer with a diameter of specified size. | Minimum: 0.000000 Default: 1.000000 |
Save Model | File path | MODEL_SAVE | Stores model to file to be used for subsequent classifications instead of training areas. | - |
Maximum Tree Depth | Integer | MAX_DEPTH | The maximum possible depth of the tree. That is the training algorithms attempts to split a node while its depth is less than maxDepth. The root node has zero depth. | Minimum: 1 Default: 10 |
Minimum Sample Count | Integer | MIN_SAMPLES | If the number of samples in a node is less than this parameter then the node will not be split. | Minimum: 2 Default: 2 |
Maximum Categories | Integer | MAX_CATEGRS | Cluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split. | Minimum: 1 Default: 10 |
Use 1SE Rule | Boolean | 1SE_RULE | If true then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. | Default: 1 |
Truncate Pruned Trees | Boolean | TRUNC_PRUNED | If true then pruned branches are physically removed from the tree. Otherwise they are retained and it is possible to get results from the original unpruned (or pruned less aggressively) tree. | Default: 1 |
Regression Accuracy | Floating point | REG_ACCURACY | Termination criteria for regression trees. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be split further. | Minimum: 0.000000 Default: 0.010000 |
Active Variable Count | Integer | ACTIVE_VARS | The size of the randomly selected subset of features at each tree node and that are used to find the best split(s). If you set it to 0 then the size will be set to the square root of the total number of features. | Minimum: 0 Default: 0 |
(*) optional |