| Tool Name | Random Forest Classification (ViGrA) |
| Tool ID | 9 |
| Library ID | imagery_vigra |
| Version | 1.0 |
| Author(s) | O.Conrad (c) 2013 |
ViGrA - Vision with Generic Algorithms
| Name | Type | Identifier | Description | Constraints |
|---|---|---|---|---|
| Input | ||||
| Features | grid list, input | FEATURES | ||
| Training Areas | shapes, input | TRAINING | ||
| Output | ||||
| Random Forest Classification | grid, output | CLASSES | ||
| Prediction Probability | grid, output, optional | PROBABILITY | ||
| Feature Probabilities | grid list, output | PROBABILITIES | ||
| Feature Importances | table, output | IMPORTANCES | ||
| Options | ||||
| Feature Probabilities | boolean | BPROBABILITIES | Default: 0 | |
| Label Field | table field | FIELD | ||
| Label is Identifier | boolean | LABEL_AS_ID | Use training area labels as identifier in classification result, assumes all label values are integer numbers! | Default: 0 |
| Minimum Redundancy Feature Selection | boolean | DO_MRMR | Use only features selected by the minimum Redundancy Maximum Relevance (mRMR) algorithm | Default: 0 |
| Number of Features | integer number | mRMR_NFEATURES | Minimum: 1 Default: 50 | |
| Discretization | boolean | mRMR_DISCRETIZE | uncheck this means no discretizaton (i.e. data is already integer) | Default: 1 |
| Discretization Threshold | floating point number | mRMR_THRESHOLD | a double number of the discretization threshold; set to 0 to make binarization | Minimum: 0.000000 Default: 1.000000 |
| Selection Method | choice | mRMR_METHOD | Available Choices: [0] Mutual Information Difference (MID) [1] Mutual Information Quotient (MIQ) 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 |