TD_DecisionForestPredict uses the following input tables.
Table | Description |
---|---|
InputTable | Contains test data, for which to predict outcomes. The input table can have no partition or PARTITION BY ANY clause. |
ModelTable | Has the same schema as the output table of TD_DecisionForest function. Model table must be a DIMENSION table, and must be from TD_DecisionForest function. |
InputTable Schema
Column | Data Type | Description |
---|---|---|
ID_Column | Varies | Unique test point identifier. Cannot be NULL. |
target_columns | INTEGER, BIGINT, SMALLINT, BYTEINT, FLOAT, DECIMAL, NUMBER | Predictor variable. Column appears once for each specified target_column. Cannot be NULL. |
accumulate_columns | Varies | Column to copy to output table. Column appears once for each specified accumulate_column. |
ModelTable Schema
Name | Data Type | Description |
---|---|---|
task_index | SMALLINT | Identifier of AMP that produced decision tree. |
tree_num | Integer | Decision tree identifier. |
tree_order | Integer | Sequence of substring of tree. |
classification_tree | VARCHAR(16000) | JSON representation of decision tree. For JSON types that can appear in the representation. |
Name | Data Type | Description |
---|---|---|
task_index | SMALLINT | Identifier of AMP that produced decision tree. |
tree_num | Integer | Decision tree identifier. |
tree_order | Integer | Sequence of substring of tree. |
regression_tree | VARCHAR(16000) | JSON representation of decision tree. For JSON types that can appear in the representation. |
JSON Type | Description |
---|---|
id_ | Node identifier. |
sum_ | Sum of values of response variable at node identified by id. Only appears for regression trees. |
sumSq_ | Sum of squared values of response variable at node identified by id. Only appears for regression trees. |
responseCounts_ | Number of observations in each class at node identified by id. Only appears for regression trees. |
size_ | Total number of observations at node identified by id. |
maxDepth_ | Maximum possible depth of tree, starting from node identified by id. For root node, the value is max_depth. For leaf nodes, the value is 0. For other nodes, maximum possible depth of tree, starting from that node. |
split_ | Start of JSON item describing a split at node identified by id. |
score_ | Gini score of node identified by id. |
attr_ | Attribute (predictor) that the algorithm split at node identified by id. |
type_ | Type of tree and split. Options are:
|
leftNodeSize_ | Number of observations assigned to left node of split. |
rightNodeSize_ | Number of observations assigned to right node of split. |
leftChild_ | Start of JSON item describing left child of node identified by id. |
rightChild_ | Start of JSON item describing right child of node identified by id. |
nodeType_ | Type of node identified by id. Options are:
|