TD_KNN Input Table for Classification
encoded |
ROW_I |
attribute_1 |
attribute_2 |
attribute_3 |
... |
attribute_49 |
sample_id |
0 |
99 |
-0.0664 |
-0.0999 |
-0.0949 |
... |
-0.0942 |
2 |
0 |
101 |
-0.603 |
-0.0938 |
-0.0900 |
... |
-0.0935 |
2 |
1 |
114 |
0.0000 |
0.0001 |
0.0001 |
... |
0.0001 |
2 |
1 |
115 |
0.0001 |
0.0001 |
0.0001 |
... |
0.0001 |
2 |
... |
... |
... |
... |
... |
... |
... |
... |
Example: TD_KNN SQL Call for Classification
CREATE VOLATILE TABLE KNN AS (
SELECT * FROM TD_KNN (
ON test_dataset AS TestTable PARTITION BY ANY
ON train_dataset AS TrainingTable DIMENSION
USING
K(3)
ResponseColumn('encoded')
InputColumns('[2:7]')
IDColumn('Row_I')
Accumulate ('encoded')
ModelType('Classification')
OutputProb('true')
EmitNeighbors('true')
Responses('0', '1')
) AS dt
)WITH DATA
ON COMMIT PRESERVE ROWS;
TD_KNN Output Table for Classification
SELECT * FROM KNN;
ROW_I |
prediction |
prob_0 |
prob_1 |
neighbor_id1 |
neighbor_id2 |
neighbor_id3 |
encoded |
43 |
1 |
0.3333 |
0.6666 |
146 |
42 |
145 |
0 |
101 |
0 |
1.0 |
0.0 |
100 |
102 |
103 |
0 |
150 |
0 |
0.6666 |
0.3333 |
48 |
47 |
128 |
1 |
192 |
1 |
0.0 |
1.0 |
191 |
193 |
190 |
1 |