| 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 number | 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 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 |