| Tool Name | Random Forest Table Classification (ViGrA) |
| Tool ID | 11 |
| Library ID | imagery_vigra |
| Version | 1.0 |
| Author(s) | B. Bechtel, O.Conrad (c) 2015 |
ViGrA - Vision with Generic Algorithms
| Name | Type | Identifier | Description | Constraints |
|---|---|---|---|---|
| Input | ||||
| Table | table, input | TABLE | Table with features, must include class-ID | |
| Output | ||||
| Feature Importances | table, output | IMPORTANCES | ||
| Options | ||||
| Features | table fields | FEATURES | Select features (table fields) for classification | |
| Prediction | table field | PREDICTION | This is field that will have the prediction results. If not set it will be added to the table. | |
| Training | table field | TRAINING | this is the table field that defines the training classes | |
| Use Label as Identifier | boolean | LABEL_AS_ID | Use training area labels as identifier in classification result, assumes all label values are integer numbers! | Default: 0 |
| Load Model | file path | RF_IMPORT | ||
| Save Model | file path | RF_EXPORT | ||
| Tree Count | integer number | RF_TREE_COUNT | How many trees to create? | Minimum: 1 Default: 32 |
| Samples per Tree | floating point number | RF_TREE_SAMPLES | Specifies the fraction of the total number of samples used per tree for learning. | Minimum: 0.000000 Maximum: 1.000000 Default: 1.000000 |
| Sample with Replacement | boolean | RF_REPLACE | Sample from training population with or without replacement? | Default: 1 |
| Minimum Node Split Size | integer number | RF_SPLIT_MIN_SIZE | Number of examples required for a node to be split. Choose 1 for complete growing. | Minimum: 1 Default: 1 |
| Features per Node | choice | RF_NODE_FEATURES | Available Choices: [0] logarithmic [1] square root [2] all Default: 1 | |
| Stratification | choice | RF_STRATIFICATION | Specifies stratification strategy. Either none, equal amount of class samples, or proportional to fraction of class samples. | Available Choices: [0] none [1] equal [2] proportional Default: 0 |