入力
このドキュメントのすべての完全なサンプルは、ダウンロード可能なzipファイルの形式で入手できます。zipファイルには、サンプルの入力テーブルを作成するSQLスクリプト ファイルが含まれています。https://docs.teradata.com/でこのドキュメントを参照している場合は、左側のサイドバーの添付ファイル
からzipファイルをダウンロードできます。- 入力テーブル: nb_iris_input_test
- モデル: nb_iris_model
モデルは、<Teradata Vantage™ Machine Learning Engine分析関数リファレンス、B700-4003>のNaive Bayesの例で作成されます。
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 |
id | 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 |
class | 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') Responses ('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 |