| Tool Name | Decision Tree Classification |
| Tool ID | 8 |
| Library ID | imagery_opencv |
| Version | 1.0 |
| Author(s) | O.Conrad (c) 2016 |
OpenCV - Open Source Computer Vision
OpenCV - Machine Learning Overview
| Name | Type | Identifier | Description | Constraints |
|---|---|---|---|---|
| Input | ||||
| Features | grid list, input | FEATURES | ||
| Training Samples | table, input | TRAIN_SAMPLES | Provide a class identifier in the first field followed by sample data corresponding to the input feature grids. | |
| 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 | ||||
| Normalize | boolean | NORMALIZE | Default: 0 | |
| Training | choice | MODEL_TRAIN | Available Choices: [0] training areas [1] training samples [2] load from file Default: 0 | |
| Class Identifier | table field | TRAIN_CLASS | ||
| Buffer Size | floating point number | TRAIN_BUFFER | For non-polygon type training areas, creates a buffer with a diameter of specified size. | Minimum: 0.000000 Default: 1.000000 |
| Load Model | file path | MODEL_LOAD | Use a model previously stored to file. | |
| Save Model | file path | MODEL_SAVE | Stores model to file to be used for subsequent classifications instead of training areas. | |
| Maximum Tree Depth | integer number | 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 number | 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 number | 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 number | 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 |