入力
- 入力テーブル: nb_iris_input_test
- モデル: nb_iris_model
モデルは、<Teradata Vantage™機械学習エンジン分析関数リファレンス、B700-4003>のNaive Bayesの例で作成されます。
入力テーブル列の説明列 |
説明 |
id |
結果の固有識別子 |
sepal_length |
Numeric |
sepal_width |
Numeric |
petal_length |
Numeric |
petal_width |
Numeric |
species |
Setosa、versicolor、またはvirginica |
nb_iris_input_testid |
sepal_length |
sepal_width |
petal_length |
petal_width |
species |
5 |
5 |
3.6 |
1.4 |
0.2 |
setosa |
10 |
4.9 |
3.1 |
1.5 |
0.1 |
setosa |
15 |
5.8 |
4 |
1.2 |
0.2 |
setosa |
20 |
5.1 |
3.8 |
1.5 |
0.3 |
setosa |
25 |
4.8 |
3.4 |
1.9 |
0.2 |
setosa |
30 |
4.7 |
3.2 |
1.6 |
0.2 |
setosa |
35 |
4.9 |
3.1 |
1.5 |
0.2 |
setosa |
40 |
5.1 |
3.4 |
1.5 |
0.2 |
setosa |
45 |
5.1 |
3.8 |
1.9 |
0.4 |
setosa |
50 |
5 |
3.3 |
1.4 |
0.2 |
setosa |
55 |
6.5 |
2.8 |
4.6 |
1.5 |
versicolor |
60 |
5.2 |
2.7 |
3.9 |
1.4 |
versicolor |
65 |
5.6 |
2.9 |
3.6 |
1.3 |
versicolor |
70 |
5.6 |
2.5 |
3.9 |
1.1 |
versicolor |
75 |
6.4 |
2.9 |
4.3 |
1.3 |
versicolor |
80 |
5.7 |
2.6 |
3.5 |
1 |
versicolor |
85 |
5.4 |
3 |
4.5 |
1.5 |
versicolor |
90 |
5.5 |
2.5 |
4 |
1.3 |
versicolor |
95 |
5.6 |
2.7 |
4.2 |
1.3 |
versicolor |
100 |
5.7 |
2.8 |
4.1 |
1.3 |
versicolor |
105 |
6.5 |
3 |
5.8 |
2.2 |
virginica |
110 |
7.2 |
3.6 |
6.1 |
2.5 |
virginica |
115 |
5.8 |
2.8 |
5.1 |
2.4 |
virginica |
120 |
6 |
2.2 |
5 |
1.5 |
virginica |
125 |
6.7 |
3.3 |
5.7 |
2.1 |
virginica |
130 |
7.2 |
3 |
5.8 |
1.6 |
virginica |
135 |
6.1 |
2.6 |
5.6 |
1.4 |
virginica |
140 |
6.9 |
3.1 |
5.4 |
2.1 |
virginica |
145 |
6.7 |
3.3 |
5.7 |
2.5 |
virginica |
150 |
5.9 |
3 |
5.1 |
1.8 |
virginica |
nb_iris_modelclass |
variable |
type |
category |
cnt |
sum |
sumSq |
totalcnt |
setosa |
sepal_width |
NUMERIC |
|
40 |
136.700000524521 |
473.290003499985 |
40 |
setosa |
petal_width |
NUMERIC |
|
40 |
10.1000002026558 |
3.03000012755394 |
40 |
setosa |
sepal_length |
NUMERIC |
|
40 |
199.900000095367 |
1004.27000005722 |
40 |
setosa |
petal_length |
NUMERIC |
|
40 |
57.6999998092651 |
84.2099996709824 |
40 |
versicolor |
sepal_width |
NUMERIC |
|
40 |
111.10000038147 |
313.130002088547 |
40 |
versicolor |
petal_width |
NUMERIC |
|
40 |
53.299999833107 |
72.7099995040894 |
40 |
versicolor |
sepal_length |
NUMERIC |
|
40 |
239.599999427795 |
1446.13999296188 |
40 |
versicolor |
petal_length |
NUMERIC |
|
40 |
172.399999141693 |
752.219992570878 |
40 |
virginica |
sepal_width |
NUMERIC |
|
40 |
118.799999952316 |
356.539999780655 |
40 |
virginica |
petal_width |
NUMERIC |
|
40 |
81.1999989748001 |
166.999995970726 |
40 |
virginica |
sepal_length |
NUMERIC |
|
40 |
264.400000572205 |
1764.92000530243 |
40 |
virginica |
petal_length |
NUMERIC |
|
40 |
222.299999713898 |
1249.1499958992 |
40 |
SQL呼び出し
DROP TABLE nb_iris_predict;
CREATE MULTISET TABLE nb_iris_predict AS (
SELECT * FROM NaiveBayesPredict (
ON nb_iris_input_test PARTITION BY ANY
ON nb_iris_model AS Model DIMENSION
USING
IDColumn ('id')
NumericInputs ('sepal_length','sepal_width','petal_length','petal_width')
ResponsesToOutput ('virginica','setosa','versicolor')
) AS dt
) WITH DATA;
出力
このクエリーは、以下のテーブルを返します。
SELECT * FROM nb_iris_predict ORDER BY 1;
出力にはテスト データ セットでの各行の予測があり、カテゴリごとの予測を行なうために使用された対数尤度の値が指定されます。
id |
prediction |
loglik_virginica |
loglik_setosa |
loglik_versicolor |
5 |
setosa |
-60.9907330174083 |
0.940424559067427 |
-38.2319825308929 |
10 |
setosa |
-61.5861966261907 |
-0.173043897170957 |
-37.6660830556247 |
15 |
setosa |
-64.7169548001753 |
-3.55476375390931 |
-42.613272284101 |
20 |
setosa |
-57.7992844148636 |
0.531796840642284 |
-35.7613053354934 |
25 |
setosa |
-55.0939143017897 |
-3.23703029869347 |
-32.1179858509341 |
30 |
setosa |
-58.0673073752287 |
0.109611164911179 |
-34.9285997859276 |
35 |
setosa |
-58.1980267787658 |
0.660202577013632 |
-34.9335988704833 |
40 |
setosa |
-58.3538858459019 |
0.976840811041703 |
-35.4425587940391 |
45 |
setosa |
-50.3847602463201 |
-4.36921429673761 |
-29.0537478266948 |
50 |
setosa |
-59.4745348026195 |
1.00257959230347 |
-36.5026022674224 |
55 |
versicolor |
-5.22108005914589 |
-270.465431908161 |
-1.7396367893394 |
60 |
versicolor |
-11.3356467465064 |
-174.565470791378 |
-2.31925264962004 |
65 |
versicolor |
-12.6496488706934 |
-138.435722453706 |
-2.1898005756116 |
70 |
versicolor |
-15.236843619572 |
-152.47255627778 |
-2.3538459106499 |
75 |
versicolor |
-8.34632493685681 |
-214.383653794905 |
-1.14727508911532 |
80 |
versicolor |
-18.455946984498 |
-109.900955754698 |
-3.72743011721095 |
85 |
versicolor |
-7.00283150694931 |
-249.656488976769 |
-2.00455589365379 |
90 |
versicolor |
-12.0279925543069 |
-177.470336291088 |
-1.74539749109463 |
95 |
versicolor |
-10.1802450220293 |
-198.037109900803 |
-1.10567314638237 |
100 |
versicolor |
-10.1315405651018 |
-187.294956922171 |
-1.02885306444447 |
105 |
virginica |
-1.58321671192447 |
-540.56351949849 |
-14.859643718252 |
110 |
virginica |
-6.11301966870239 |
-654.801984259278 |
-28.8385135092999 |
115 |
virginica |
-3.64635253153959 |
-456.647579953406 |
-15.3298808321577 |
120 |
versicolor |
-7.73615017754911 |
-322.909009762056 |
-3.53629430321742 |
125 |
virginica |
-1.87627054598219 |
-509.817023097936 |
-13.7515396871732 |
130 |
virginica |
-3.36908052149115 |
-469.802937074554 |
-9.13832860900173 |
135 |
versicolor |
-5.81482980902253 |
-403.678170868448 |
-4.51644862072851 |
140 |
virginica |
-1.48430911768034 |
-463.610989255182 |
-12.0238603485835 |
145 |
virginica |
-3.82266629516761 |
-576.395460020916 |
-22.6942168473031 |
150 |
virginica |
-2.57004648415525 |
-366.506113945482 |
-4.84887216455807 |