The following data set is used in the examples.
id |
MedInc |
HouseAge |
AveRooms |
AveBedrms |
Population |
AveOccup |
Latitude |
Longitude |
MedHouseVal |
14870 |
1.858 |
23 |
3.901 |
1.077 |
1025 |
2.47 |
32.64 |
-117.11 |
0.675 |
6044 |
2.114 |
27 |
3.855 |
1.072 |
1024 |
4.633 |
34.05 |
-117.74 |
1.109 |
3593 |
6.654 |
32 |
6.331 |
0.995 |
1285 |
3.104 |
34.24 |
-118.48 |
2.676 |
9454 |
1.228 |
25 |
5.504 |
1.154 |
991 |
2.629 |
39.77 |
-123.23 |
0.603 |
18760 |
3.282 |
16 |
5.998 |
1.076 |
1414 |
3.081 |
40.6 |
-122.25 |
1.283 |
11670 |
4.5 |
28 |
5.102 |
1.044 |
2112 |
2.63 |
33.84 |
-118.01 |
2.021 |
17768 |
2.756 |
29 |
4.53 |
1.04 |
3572 |
4.603 |
37.35 |
-121.85 |
1.601 |
244 |
2.391 |
44 |
4.866 |
1.164 |
2269 |
3.72 |
37.78 |
-122.22 |
1.117 |
5328 |
2.768 |
23 |
3.039 |
1.064 |
2031 |
1.637 |
34.04 |
-118.45 |
2.775 |
14365 |
2.164 |
43 |
4.533 |
0.995 |
392 |
1.867 |
32.72 |
-117.23 |
2.442 |
2313 |
2.486 |
15 |
5.468 |
1.045 |
649 |
2.449 |
36.94 |
-119.7 |
0.863 |
12342 |
2.588 |
28 |
6.268 |
1.372 |
3470 |
2.59 |
33.84 |
-116.53 |
1.59 |
6558 |
6.827 |
36 |
7.021 |
1.036 |
1897 |
2.71 |
34.2 |
-118.11 |
3.594 |
17157 |
9.78 |
20 |
6.678 |
0.918 |
324 |
2.219 |
37.43 |
-122.21 |
5 |
18099 |
5.753 |
27 |
6.437 |
1.027 |
1259 |
2.868 |
37.32 |
-122.01 |
4.314 |
Model
attribute |
predictor |
estimate |
value |
-17 |
OneClass SVM |
NaN |
FALSE |
-16 |
Kernel |
NaN |
LINEAR |
-15 |
Intercept Scaling |
1.000000 |
None |
-14 |
Epsilon |
0.100000 |
None |
-13 |
LocalSGD Iterations |
0.000000 |
None |
-12 |
Nesterov |
NaN |
FALSE |
-11 |
Momentum |
0.000000 |
None |
-10 |
Learning Rate (Final) |
0.204246 |
None |
-9 |
Learning Rate (Initial) |
0.050000 |
None |
-8 |
Number of Iterations |
227.000000 |
CONVERGED |
-7 |
Alpha |
0.150000 |
Elasticnet |
-6 |
Regularization |
0.020000 |
ENABLED |
-5 |
BIC |
5.572674 |
None |
-4 |
AIC |
-0.799777 |
None |
-3 |
Number of Observations |
15.000000 |
None |
-2 |
MSE |
0.285556 |
None |
-1 |
Loss Function |
NaN |
EPSILON_INSENSITIVE |
0 |
(Intercept) |
2.191718 |
None |
1 |
MedInc |
1.226729 |
None |
2 |
HouseAge |
.0232213 |
None |
3 |
AveRooms |
-.332659 |
None |
4 |
AveBedrms |
0.000000 |
None |
5 |
Population |
0.113633 |
None |
6 |
AveOccup |
-0.286434 |
None |
7 |
Latitude |
-0.261509 |
None |
8 |
Longitude |
-0.198519 |
None |
Example: TD_SVMPredict Call Using Regression
CREATE VOLATILE TABLE svm_m0del_predict_cal_housing AS (
SELECT * from TD_SVMPredict (
ON cal_housing_ex_scaled AS INPUTTABLE
ON svm_model_cal_housing AS ModelTable DIMENSION
USING
IDColumn ('id')
Accumulate('MedHouseVal')
) AS dt
) WITH DATA
ON COMMIT PRESERVE ROWS;
TD_SVMPredict Output for Regression
id |
prediction |
MedHouseVal |
18760 |
1.268192 |
1.28300 |
244 |
1.815538 |
1.11700 |
9454 |
0.570297 |
0.60300 |
14365 |
2.280287 |
2.44200 |
6558 |
3.628097 |
3.59400 |
12342 |
1.506883 |
1.59000 |
5328 |
2.709805 |
2.77500 |
14870 |
1.601341 |
0.67500 |
17768 |
1.546305 |
1.60100 |
18099 |
2.855638 |
4.31400 |
2313 |
0.976091 |
0.86300 |
3593 |
3.445194 |
2.67600 |
6044 |
1.025779 |
1.10900 |
11670 |
2.814743 |
2.02100 |
17157 |
4.831587 |
5.00001 |
Classification iris Data Set
id |
sepal_length |
sepal_width |
petal_length |
petal_width |
variety |
label |
61 |
5.0 |
2.0 |
3.5 |
1.0 |
Versicolor |
0 |
51 |
7.0 |
3.2 |
4.7 |
1.4 |
Versicolor |
0 |
50 |
5.1 |
3.4 |
1.5 |
0.2 |
Setosa |
1 |
59 |
6.6 |
2.9 |
4.6 |
1.3 |
Versicolor |
0 |
38 |
4.9 |
3.6 |
1.4 |
0.1 |
Setosa |
1 |
... |
... |
... |
... |
... |
... |
... |
90 |
5.5 |
2.5 |
4.0 |
1.3 |
Versicolor |
0 |
67 |
5.6 |
3.0 |
4.5 |
1.5 |
Versicolor |
0 |
44 |
5.0 |
3.5 |
1.6 |
0.6 |
Setosa |
1 |
42 |
4.5 |
2.3 |
1.3 |
0.3 |
Setosa |
1 |
82 |
5.5 |
2.4 |
3.7 |
1.0 |
Versicolor |
0 |
Example: TD_SVMPredict Call Using Classification
CREATE VOLATILE TABLE svm_model_predict_iris_data AS (
SELECT * FROM TD_SVMPredict (
ON iris_data AS INPUTTABLE
ON svm_model_iris_data AS ModelTable DIMENSION
USING
IdColumn('id')
Accumulate('label')
OutputProb('true')
Responses('0','1')
) AS dt
) WITH DATA
ON COMMIT PRESERVE ROWS;
TD_SVMPredict Output for Classification
id |
prediction |
prob_0 |
prob_1 |
label |
61 |
0.0 |
0.916982 |
0.083018 |
0 |
51 |
0.0 |
0.947352 |
0.052648 |
0 |
40 |
1.0 |
0.106359 |
0.893641 |
1 |
59 |
0.0 |
0.954081 |
0.045919 |
0 |
38 |
1.0 |
0.077917 |
0.922083 |
1 |
... |
... |
... |
... |
... |
90 |
0.0 |
0.938794 |
0.061206 |
0 |
67 |
0.0 |
0.955700 |
0.044300 |
0 |
44 |
1.0 |
0.135795 |
0.864205 |
1 |
42 |
1.0 |
0.220665 |
0.779335 |
1 |
82 |
0.0 |
0.903738 |
0.096262 |
0 |