入力
NaiveBayesTextClassifierPredictの例: TopKを指定と同様に、次のようになります。
- PredictorValues: complaints_test_tokenized
- モデル: complaints_tokens_model
SQL呼び出し
SELECT * FROM NaiveBayesTextClassifierPredict (
ON complaints_test_tokenized AS PredictorValues PARTITION BY doc_id
ON complaints_tokens_model AS Model DIMENSION
USING
ModelType ('Bernoulli')
InputTokenColumn ('token')
DocIDColumns ('doc_id')
OutputProb ('true')
Accumulate ('doc_name')
Responses ('crash', 'no crash')
) AS dt ORDER BY doc_id;
出力
doc_id prediction loglik_crash loglik_no_crash prob_crash prob_no_crash doc_name
------ ---------- ---------------------- ---------------------- ---------------------- ---------------------- --------
1 no_crash -1.38044220625651E 002 -1.17666267644292E 002 1.41243173571687E-009 9.99999998587568E-001 A
2 no_crash -1.04652470718918E 002 -9.82811865081127E 001 1.70704288519507E-003 9.98292957114805E-001 B
3 no_crash -1.03026451289745E 002 -7.62146044204976E 001 2.26862573862878E-012 9.99999999997731E-001 C
4 no_crash -1.10830711173169E 002 -8.58531176043404E 001 1.42026355157382E-011 9.99999999985797E-001 D
5 crash -1.20601083912966E 002 -1.23936921216052E 002 9.65637986161646E-001 3.43620138383542E-002 E
6 no_crash -1.30310015371040E 002 -1.17454141890718E 002 2.61074198636704E-006 9.99997389258014E-001 F
7 crash -1.20005517060745E 002 -1.23123774759574E 002 9.57639606312734E-001 4.23603936872661E-002 G
8 no_crash -1.08617321658980E 002 -9.00827983614664E 001 8.92398441816595E-009 9.99999991076016E-001 H
9 no_crash -1.19919230739025E 002 -1.16147101713878E 002 2.24857954852037E-002 9.77514204514796E-001 I
10 no_crash -1.06104244132225E 002 -8.97078469668254E 001 7.57068462691010E-008 9.99999924293154E-001 J