Random Forest Classification (ViGrA)
- Author: O.Conrad (c) 2013
- Menu: Imagery | ViGrA [deprecated]
Description
Random Forest Classification.
References
Parameters
| 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 | Grid System | grid system | PARAMETERS_GRID_SYSTEM | - | - |
| Feature Probabilities | boolean | BPROBABILITIES | - | Default: 0 | |
| Label Field | table field | FIELD | - | - | |
| 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 | |
| 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 | |
| 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 |
Command Line
Usage: saga_cmd imagery_vigra 9 [-FEATURES] [-CLASSES ] [-PROBABILITY ] [-BPROBABILITIES ] [-PROBABILITIES ] [-IMPORTANCES ] [-TRAINING ] [-FIELD ] [-LABEL_AS_ID ] [-DO_MRMR ] [-mRMR_NFEATURES ] [-mRMR_DISCRETIZE ] [-mRMR_THRESHOLD ] [-mRMR_METHOD ] [-RF_TREE_COUNT ] [-RF_TREE_SAMPLES ] [-RF_REPLACE ] [-RF_SPLIT_MIN_SIZE ] [-RF_NODE_FEATURES ] [-RF_STRATIFICATION ] -FEATURES: Features grid list, input -CLASSES: Random Forest Classification grid, output -PROBABILITY: Prediction Probability grid, output, optional -BPROBABILITIES: Feature Probabilities boolean Default: 0 -PROBABILITIES: Feature Probabilities grid list, output -IMPORTANCES: Feature Importances table, output -TRAINING: Training Areas shapes, input -FIELD: Label Field table field -LABEL_AS_ID: Use Label as Identifier boolean Default: 0 -DO_MRMR: Minimum Redundancy Feature Selection boolean Default: 0 -mRMR_NFEATURES: Number of Features integer number Minimum: 1 Default: 50 -mRMR_DISCRETIZE: Discretization boolean Default: 1 -mRMR_THRESHOLD: Discretization Threshold floating point number Minimum: 0.000000 Default: 1.000000 -mRMR_METHOD: Selection Method choice Available Choices: [0] Mutual Information Difference (MID) [1] Mutual Information Quotient (MIQ) Default: 0 -RF_TREE_COUNT: Tree Count integer number Minimum: 1 Default: 32 -RF_TREE_SAMPLES: Samples per Tree floating point number Minimum: 0.000000 Maximum: 1.000000 Default: 1.000000 -RF_REPLACE: Sample with Replacement boolean Default: 1 -RF_SPLIT_MIN_SIZE: Minimum Node Split Size integer number Minimum: 1 Default: 1 -RF_NODE_FEATURES: Features per Node choice Available Choices: [0] logarithmic [1] square root [2] all Default: 1 -RF_STRATIFICATION: Stratification choice Available Choices: [0] none [1] equal [2] proportional Default: 0