| Name | Type | Identifier | Description | Constraints |
Input | Features | Grid list, input | FEATURES | - | - |
Presence Data | Shapes, input | PRESENCE | - | - |
Output | Presence Prediction | Grid, output | PREDICTION | - | - |
Presence Probability (*) | Grid, output, optional | PROBABILITY | - | - |
Options | Grid System | Grid system | PARAMETERS_GRID_SYSTEM | - | - |
Background Sample Density [Percent] | Floating point | BACKGROUND | - | Minimum: 0.000000 Maximum: 100.000000 Default: 1.000000 |
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 | 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 | 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 | RF_TREE_COUNT | How many trees to create? | Minimum: 1 Default: 32 |
Samples per Tree | Floating point | 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 | 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 |
(*) optional |