For examples that use the syntax elements TopK and Responses, see SVMDensePredict Examples.
Input
- InputTable: svm_iris_input_test
- Model: svm_iris_model, the SVMSparse Example output table
id | species | attribute | value1 |
---|---|---|---|
5 | setosa | sepal_length | 5.0 |
5 | setosa | sepal_width | 3.6 |
5 | setosa | petal_length | 1.4 |
5 | setosa | petal_width | 0.2 |
10 | setosa | sepal_length | 4.9 |
10 | setosa | sepal_width | 3.1 |
10 | setosa | petal_length | 1.5 |
10 | setosa | petal_width | 0.1 |
15 | setosa | sepal_length | 5.8 |
15 | setosa | sepal_width | 4.0 |
15 | setosa | petal_length | 1.2 |
15 | setosa | petal_width | 0.2 |
... | ... | ... | ... |
SQL Call
CREATE MULTISET TABLE svm_iris_predict_out AS ( SELECT * FROM SVMSparsePredict_MLE ( ON svm_iris_input_test AS InputTable PARTITION BY id ON svm_iris_model AS Model DIMENSION USING IDColumn ('id') AttributeNameColumn ('attribute') AttributeValueColumn ('value1') Accumulate ('species') ) AS dt ) WITH DATA;
Output
SELECT * FROM svm_iris_predict_out ORDER BY id;
id predict_value predict_confidence species --- ------------- ------------------ ---------- 5 setosa 0.9712114283894899 setosa 10 setosa 0.891209210996914 setosa 15 setosa 0.9884800781364631 setosa 20 setosa 0.9801252987281942 setosa 25 setosa 0.9009144408735393 setosa 30 setosa 0.9156565763906245 setosa 35 setosa 0.9070437281039562 setosa 40 setosa 0.9454114427327119 setosa 45 setosa 0.9647235775345661 setosa 50 setosa 0.946083635829095 setosa 55 versicolor 0.7913472663302237 versicolor 60 virginica 0.4508441718655297 versicolor 65 versicolor 0.5617925890708627 versicolor 70 versicolor 0.8426985427898467 versicolor 75 versicolor 0.8489260026561855 versicolor 80 versicolor 0.8069899264075321 versicolor 85 virginica 0.6671521277894518 versicolor 90 versicolor 0.6840553604630605 versicolor 95 versicolor 0.6749299918507788 versicolor 100 versicolor 0.6965141822869577 versicolor 105 virginica 0.9641473480486245 virginica 110 virginica 0.9733659600265973 virginica 115 virginica 0.9929219369972574 virginica 120 versicolor 0.5344118196177321 virginica 125 virginica 0.9048321676266392 virginica 130 versicolor 0.7838136004714887 virginica 135 versicolor 0.5176868161785055 virginica 140 virginica 0.7979779423920981 virginica 145 virginica 0.9851460930914178 virginica 150 virginica 0.8614044973987343 virginica
Prediction Accuracy
SELECT (SELECT count(id) FROM svm_iris_predict_out WHERE predict_value = species)/(1.00 * ( SELECT count(id) FROM svm_iris_predict_out) ) AS prediction_accuracy;
prediction_accuracy ------------------- 0.83
Download a zip file of all examples and a SQL script file that creates their input tables.