入力
- 入力テーブル: housing_test、14の変数の54の結果があります。
- モデル: rft_model、Teradata Vantage™ Machine Learning Engine分析関数リファレンス、B700-4003の「DecisionForestの例: TreeType ('classification')およびOutOfBag ('false')」によって出力される
列 | 説明 |
---|---|
sn | 販売番号(結果の固有識別子) |
price | 米国ドルでの販売価(数値) |
lotsize | 平方フィートでのロットサイズ(数値) |
bedrooms | 寝室数(数値) |
bathrms | バスルーム一式の数(数値) |
stories | 階数、地下を除く(数値) |
driveway | 家に私道があるかどうか—yesまたはno(カテゴリ別) |
recroom | 家にレクリエーション ルームがあるかどうか—yesまたはno(カテゴリ別) |
fullbase | 家に完全装備の地階があるかどうか—yesまたはno(カテゴリ別) |
gashw | 家の給湯がガスかどうか—yesまたはno(カテゴリ別) |
airco | 家が集中型空調を備えているかどうか—yesまたはno(カテゴリ別) |
garagepl | ガレージの数(数値) |
prefarea | 家の周囲が好ましい環境かどうか—yesまたはno(カテゴリ別) |
homestyle | 家の様式(応答変数) |
sn | price | lotsize | bedrooms | bathrms | stories | driveway | recroom | fullbase | gashw | airco | garagepl | prefarea | homestyle |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
13 | 27000 | 1700 | 3 | 1 | 2 | yes | no | no | no | no | 0 | no | Classic |
16 | 37900 | 3185 | 2 | 1 | 1 | yes | no | no | no | yes | 0 | no | Classic |
25 | 42000 | 4960 | 2 | 1 | 1 | yes | no | no | no | no | 0 | no | Classic |
38 | 67000 | 5170 | 3 | 1 | 4 | yes | no | no | no | yes | 0 | no | Eclectic |
53 | 68000 | 9166 | 2 | 1 | 1 | yes | no | yes | no | yes | 2 | no | Eclectic |
104 | 132000 | 3500 | 4 | 2 | 2 | yes | no | no | yes | no | 2 | no | bungalow |
111 | 43000 | 5076 | 3 | 1 | 1 | no | no | no | no | no | 0 | no | Classic |
117 | 93000 | 3760 | 3 | 1 | 2 | yes | no | no | yes | no | 2 | no | Eclectic |
132 | 44500 | 3850 | 3 | 1 | 2 | yes | no | no | no | no | 0 | no | Classic |
140 | 43000 | 3750 | 3 | 1 | 2 | yes | no | no | no | no | 0 | no | Classic |
142 | 40000 | 2650 | 3 | 1 | 2 | yes | no | yes | no | no | 1 | no | Classic |
157 | 60000 | 2953 | 3 | 1 | 2 | yes | no | yes | no | yes | 0 | no | Eclectic |
..。 | ..。 | ..。 | ..。 | ..。 | ..。 | ..。 | ..。 | ..。 | ..。 | ..。 | ..。 | ..。 | ..。 |
worker_ip | task_index | tree_num | CAST(tree AS VARCHAR(50)) |
---|---|---|---|
xx.xx.xx.xx | 0 | 0 | {"responseCounts_":{"Eclectic":148,"bungalow":30," |
xx.xx.xx.xx | 0 | 1 | {"responseCounts_":{"Eclectic":158,"bungalow":26," |
xx.xx.xx.xx | 0 | 2 | {"responseCounts_":{"Eclectic":120,"bungalow":38," |
xx.xx.xx.xx | 0 | 3 | {"responseCounts_":{"Eclectic":166,"bungalow":29," |
xx.xx.xx.xx | 0 | 4 | {"responseCounts_":{"Eclectic":138,"bungalow":32," |
xx.xx.xx.xx | 0 | 5 | {"responseCounts_":{"Eclectic":158,"bungalow":34," |
xx.xx.xx.xx | 0 | 6 | {"responseCounts_":{"Eclectic":168,"bungalow":32," |
xx.xx.xx.xx | 0 | 7 | {"responseCounts_":{"Eclectic":145,"bungalow":40," |
xx.xx.xx.xx | 0 | 8 | {"responseCounts_":{"Eclectic":150,"bungalow":34," |
xx.xx.xx.xx | 0 | 9 | {"responseCounts_":{"Eclectic":156,"bungalow":42," |
xx.xx.xx.xx | 0 | 10 | {"responseCounts_":{"Eclectic":148,"bungalow":18," |
xx.xx.xx.xx | 0 | 11 | {"responseCounts_":{"Eclectic":147,"bungalow":20," |
xx.xx.xx.xx | 0 | 12 | {"responseCounts_":{"Eclectic":150,"bungalow":31," |
xx.xx.xx.xx | 0 | 13 | {"responseCounts_":{"Eclectic":135,"bungalow":32," |
xx.xx.xx.xx | 0 | 14 | {"responseCounts_":{"Eclectic":139,"bungalow":24," |
xx.xx.xx.xx | 0 | 15 | {"responseCounts_":{"Eclectic":146,"bungalow":27," |
xx.xx.xx.xx | 0 | 16 | {"responseCounts_":{"Eclectic":152,"bungalow":23," |
xx.xx.xx.xx | 0 | 17 | {"responseCounts_":{"Eclectic":135,"bungalow":23," |
xx.xx.xx.xx | 0 | 18 | {"responseCounts_":{"Eclectic":148,"bungalow":29," |
xx.xx.xx.xx | 0 | 19 | {"responseCounts_":{"Eclectic":166,"bungalow":33," |
xx.xx.xx.xx | 0 | 20 | {"responseCounts_":{"Eclectic":142,"bungalow":28," |
xx.xx.xx.xx | 0 | 21 | {"responseCounts_":{"Eclectic":172,"bungalow":27," |
xx.xx.xx.xx | 0 | 22 | {"responseCounts_":{"Eclectic":147,"bungalow":37," |
xx.xx.xx.xx | 0 | 23 | {"responseCounts_":{"Eclectic":158,"bungalow":31," |
xx.xx.xx.xx | 0 | 24 | {"responseCounts_":{"Eclectic":158,"bungalow":33," |
xx.xx.xx.xx | 1 | 0 | {"responseCounts_":{"Eclectic":140,"bungalow":44," |
xx.xx.xx.xx | 1 | 1 | {"responseCounts_":{"Eclectic":161,"bungalow":28," |
xx.xx.xx.xx | 1 | 2 | {"responseCounts_":{"Eclectic":131,"bungalow":25," |
xx.xx.xx.xx | 1 | 3 | {"responseCounts_":{"Eclectic":167,"bungalow":28," |
xx.xx.xx.xx | 1 | 4 | {"responseCounts_":{"Eclectic":150,"bungalow":19," |
xx.xx.xx.xx | 1 | 5 | {"responseCounts_":{"Eclectic":158,"bungalow":24," |
xx.xx.xx.xx | 1 | 6 | {"responseCounts_":{"Eclectic":177,"bungalow":32," |
xx.xx.xx.xx | 1 | 7 | {"responseCounts_":{"Eclectic":156,"bungalow":24," |
xx.xx.xx.xx | 1 | 8 | {"responseCounts_":{"Eclectic":156,"bungalow":37," |
xx.xx.xx.xx | 1 | 9 | {"responseCounts_":{"Eclectic":165,"bungalow":24," |
xx.xx.xx.xx | 1 | 10 | {"responseCounts_":{"Eclectic":135,"bungalow":29," |
xx.xx.xx.xx | 1 | 11 | {"responseCounts_":{"Eclectic":140,"bungalow":20," |
xx.xx.xx.xx | 1 | 12 | {"responseCounts_":{"Eclectic":156,"bungalow":24," |
xx.xx.xx.xx | 1 | 13 | {"responseCounts_":{"Eclectic":147,"bungalow":34," |
xx.xx.xx.xx | 1 | 14 | {"responseCounts_":{"Eclectic":151,"bungalow":22," |
xx.xx.xx.xx | 1 | 15 | {"responseCounts_":{"Eclectic":161,"bungalow":18," |
xx.xx.xx.xx | 1 | 16 | {"responseCounts_":{"Eclectic":156,"bungalow":19," |
xx.xx.xx.xx | 1 | 17 | {"responseCounts_":{"Eclectic":126,"bungalow":29," |
xx.xx.xx.xx | 1 | 18 | {"responseCounts_":{"Eclectic":148,"bungalow":26," |
xx.xx.xx.xx | 1 | 19 | {"responseCounts_":{"Eclectic":177,"bungalow":21," |
xx.xx.xx.xx | 1 | 20 | {"responseCounts_":{"Eclectic":137,"bungalow":31," |
xx.xx.xx.xx | 1 | 21 | {"responseCounts_":{"Eclectic":171,"bungalow":28," |
xx.xx.xx.xx | 1 | 22 | {"responseCounts_":{"Eclectic":146,"bungalow":30," |
xx.xx.xx.xx | 1 | 23 | {"responseCounts_":{"Eclectic":149,"bungalow":21," |
xx.xx.xx.xx | 1 | 24 | {"responseCounts_":{"Eclectic":158,"bungalow":18," |
SQL呼び出し
Accumulate構文要素を使用してhomestyle変数を渡し、結果ごとに実際の応答と予想される応答を容易に比較します。
CREATE MULTISET TABLE rf_housing_predict AS ( SELECT * FROM DecisionForestPredict ( ON housing_test PARTITION BY ANY ON rft_model AS Model DIMENSION USING NumericInputs ('price ','lotsize ','bedrooms ','bathrms ', 'stories ','garagepl') CategoricalInputs ('driveway ','recroom ','fullbase ','gashw ', 'airco ','prefarea') IdColumn ('sn') Accumulate ('homestyle') Detailed ('false') ) 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 rf_housing_predict ORDER BY 2;
homestyle sn prediction confidence_lower confidence_upper ------------------------------ ----------- -------------------- ---------------------- ---------------------- classic 13 classic 8.88888888888889E-001 8.88888888888889E-001 classic 16 classic 8.88888888888889E-001 8.88888888888889E-001 classic 25 classic 1.00000000000000E 000 1.00000000000000E 000 eclectic 38 eclectic 7.77777777777778E-001 7.77777777777778E-001 eclectic 53 eclectic 7.77777777777778E-001 7.77777777777778E-001 bungalow 104 eclectic 7.77777777777778E-001 7.77777777777778E-001 classic 111 classic 1.00000000000000E 000 1.00000000000000E 000 eclectic 117 eclectic 1.00000000000000E 000 1.00000000000000E 000 classic 132 classic 8.88888888888889E-001 8.88888888888889E-001 classic 140 classic 8.88888888888889E-001 8.88888888888889E-001 classic 142 classic 8.88888888888889E-001 8.88888888888889E-001 eclectic 157 eclectic 1.00000000000000E 000 1.00000000000000E 000 eclectic 161 eclectic 1.00000000000000E 000 1.00000000000000E 000 bungalow 162 bungalow 5.55555555555556E-001 5.55555555555556E-001 eclectic 176 eclectic 1.00000000000000E 000 1.00000000000000E 000 eclectic 177 eclectic 1.00000000000000E 000 1.00000000000000E 000 classic 195 classic 1.00000000000000E 000 1.00000000000000E 000 classic 198 classic 8.88888888888889E-001 8.88888888888889E-001 eclectic 224 eclectic 1.00000000000000E 000 1.00000000000000E 000 classic 234 classic 1.00000000000000E 000 1.00000000000000E 000 classic 237 classic 8.88888888888889E-001 8.88888888888889E-001 classic 239 classic 1.00000000000000E 000 1.00000000000000E 000 classic 249 classic 1.00000000000000E 000 1.00000000000000E 000 classic 251 classic 8.88888888888889E-001 8.88888888888889E-001 eclectic 254 eclectic 8.88888888888889E-001 8.88888888888889E-001 eclectic 255 eclectic 1.00000000000000E 000 1.00000000000000E 000 classic 260 classic 1.00000000000000E 000 1.00000000000000E 000 eclectic 274 eclectic 8.88888888888889E-001 8.88888888888889E-001 classic 294 classic 1.00000000000000E 000 1.00000000000000E 000 eclectic 301 eclectic 1.00000000000000E 000 1.00000000000000E 000 eclectic 306 eclectic 1.00000000000000E 000 1.00000000000000E 000 eclectic 317 eclectic 7.77777777777778E-001 7.77777777777778E-001 bungalow 329 bungalow 8.88888888888889E-001 8.88888888888889E-001 bungalow 339 bungalow 5.55555555555556E-001 5.55555555555556E-001 eclectic 340 eclectic 1.00000000000000E 000 1.00000000000000E 000 eclectic 353 eclectic 1.00000000000000E 000 1.00000000000000E 000 eclectic 355 eclectic 8.88888888888889E-001 8.88888888888889E-001 eclectic 364 eclectic 1.00000000000000E 000 1.00000000000000E 000 bungalow 367 bungalow 7.77777777777778E-001 7.77777777777778E-001 bungalow 377 bungalow 7.77777777777778E-001 7.77777777777778E-001 eclectic 401 eclectic 8.88888888888889E-001 8.88888888888889E-001 eclectic 403 eclectic 1.00000000000000E 000 1.00000000000000E 000 eclectic 408 eclectic 1.00000000000000E 000 1.00000000000000E 000 eclectic 411 eclectic 1.00000000000000E 000 1.00000000000000E 000 eclectic 440 eclectic 8.88888888888889E-001 8.88888888888889E-001 eclectic 441 eclectic 1.00000000000000E 000 1.00000000000000E 000 eclectic 443 eclectic 1.00000000000000E 000 1.00000000000000E 000 classic 459 classic 8.88888888888889E-001 8.88888888888889E-001 classic 463 classic 7.77777777777778E-001 7.77777777777778E-001 eclectic 469 eclectic 7.77777777777778E-001 7.77777777777778E-001 eclectic 472 eclectic 1.00000000000000E 000 1.00000000000000E 000 bungalow 527 bungalow 7.77777777777778E-001 7.77777777777778E-001 bungalow 530 eclectic 6.66666666666667E-001 6.66666666666667E-001 eclectic 540 eclectic 8.88888888888889E-001 8.88888888888889E-001
予測精度
このクエリーは予測精度を返します。
SELECT (SELECT count(sn) FROM rf_housing_predict WHERE homestyle = prediction) / (SELECT count(sn) FROM rf_housing_predict) AS PA;
pa |
---|
0.77777777777777777778 |