Random Forest Table Classification.
| | 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 |
| 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 |