分類判別機器學習模型可以用於線上識別或者判別系統對象的分類，比如識別每一個使用者的請求是否為攻擊性的請求。也可以用於識別因素之間的關聯關係。本文介紹分類判別分析函數的基本文法和樣本。
背景資訊
本文介紹的分類判別函數樣本索引如圖所示。更多資訊，請參見建立索引。
日誌範例如下：
1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126,83,77,4200,None 2,Male,28,Doctor,6.2,6,60,8,Normal,125,80,75,10000,None 3,Male,28,Doctor,6.2,6,60,8,Normal,125,80,75,10000,None 4,Male,28,Sales Representative,5.9,4,30,8,Obese,140,90,85,3000,Sleep Apnea 5,Male,28,Sales Representative,5.9,4,30,8,Obese,140,90,85,3000,Sleep Apnea 6,Male,28,Software Engineer,5.9,4,30,8,Obese,140,90,85,3000,Insomnia 7,Male,29,Teacher,6.3,6,40,7,Obese,140,90,82,3500,Insomnia 8,Male,29,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None 9,Male,29,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None 10,Male,29,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None 11,Male,29,Doctor,6.1,6,30,8,Normal,120,80,70,8000,None 12,Male,29,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None 13,Male,29,Doctor,6.1,6,30,8,Normal,120,80,70,8000,None 14,Male,29,Doctor,6,6,30,8,Normal,120,80,70,8000,None 15,Male,29,Doctor,6,6,30,8,Normal,120,80,70,8000,None 16,Male,29,Doctor,6,6,30,8,Normal,120,80,70,8000,None 17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132,87,80,4000,Sleep Apnea 18,Male,29,Doctor,6,6,30,8,Normal,120,80,70,8000,Sleep Apnea 19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132,87,80,4000,Insomnia 20,Male,30,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None 21,Male,30,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 22,Male,30,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 23,Male,30,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 24,Male,30,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 25,Male,30,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None 26,Male,30,Doctor,7.9,7,75,6,Normal,120,80,70,8000,None 27,Male,30,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None 28,Male,30,Doctor,7.9,7,75,6,Normal,120,80,70,8000,None 29,Male,30,Doctor,7.9,7,75,6,Normal,120,80,70,8000,None 30,Male,30,Doctor,7.9,7,75,6,Normal,120,80,70,8000,None 31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130,86,78,4100,Sleep Apnea 32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130,86,78,4100,Insomnia 33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117,76,69,6800,None 34,Male,31,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None 35,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 36,Male,31,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None 37,Male,31,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None 38,Male,31,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None 39,Male,31,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None 40,Male,31,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None 41,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 42,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 43,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 44,Male,31,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None 45,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 46,Male,31,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None 47,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 48,Male,31,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None 49,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 50,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,Sleep Apnea 51,Male,32,Engineer,7.5,8,45,3,Normal,120,80,70,8000,None 52,Male,32,Engineer,7.5,8,45,3,Normal,120,80,70,8000,None 53,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None 54,Male,32,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None 55,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None 56,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None 57,Male,32,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 58,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None 59,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None 60,Male,32,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None 61,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None 62,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None 63,Male,32,Doctor,6.2,6,30,8,Normal,125,80,72,5000,None 64,Male,32,Doctor,6.2,6,30,8,Normal,125,80,72,5000,None 65,Male,32,Doctor,6.2,6,30,8,Normal,125,80,72,5000,None 66,Male,32,Doctor,6.2,6,30,8,Normal,125,80,72,5000,None 67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118,76,68,7000,None 68,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,Insomnia 69,Female,33,Scientist,6.2,6,50,6,Overweight,128,85,76,5500,None 70,Female,33,Scientist,6.2,6,50,6,Overweight,128,85,76,5500,None 71,Male,33,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None 72,Male,33,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None 73,Male,33,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None 74,Male,33,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None 75,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None 76,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None 77,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None 78,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None 79,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None 80,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None 81,Female,34,Scientist,5.8,4,32,8,Overweight,131,86,81,5200,Sleep Apnea 82,Female,34,Scientist,5.8,4,32,8,Overweight,131,86,81,5200,Sleep Apnea 83,Male,35,Teacher,6.7,7,40,5,Overweight,128,84,70,5600,None 84,Male,35,Teacher,6.7,7,40,5,Overweight,128,84,70,5600,None 85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120,80,70,8000,None 86,Female,35,Accountant,7.2,8,60,4,Normal,115,75,68,7000,None 87,Male,35,Engineer,7.2,8,60,4,Normal,125,80,65,5000,None 88,Male,35,Engineer,7.2,8,60,4,Normal,125,80,65,5000,None 89,Male,35,Engineer,7.3,8,60,4,Normal,125,80,65,5000,None 90,Male,35,Engineer,7.3,8,60,4,Normal,125,80,65,5000,None 91,Male,35,Engineer,7.3,8,60,4,Normal,125,80,65,5000,None 92,Male,35,Engineer,7.3,8,60,4,Normal,125,80,65,5000,None 93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120,80,70,8000,None 94,Male,35,Lawyer,7.4,7,60,5,Obese,135,88,84,3300,Sleep Apnea 95,Female,36,Accountant,7.2,8,60,4,Normal,115,75,68,7000,Insomnia 96,Female,36,Accountant,7.1,8,60,4,Normal,115,75,68,7000,None 97,Female,36,Accountant,7.2,8,60,4,Normal,115,75,68,7000,None 98,Female,36,Accountant,7.1,8,60,4,Normal,115,75,68,7000,None 99,Female,36,Teacher,7.1,8,60,4,Normal,115,75,68,7000,None 100,Female,36,Teacher,7.1,8,60,4,Normal,115,75,68,7000,None 101,Female,36,Teacher,7.2,8,60,4,Normal,115,75,68,7000,None
分類判別分析函數列表
分類判別分析函數使用分類判別機器學習模型，分類判別機器學習模型可以用於線上識別或者判別系統對象的分類。
函數名稱 | 文法 | 說明 | 傳回值類型 |
decision_tree_classifier( target_variable varchar, input_variable_array array(varchar), target_variable_name varchar, input_variable_name_array array(varchar), input_variable_type_array array(varchar), <optional> model_options varchar ) | 根據最近輸入的一批資料樣本，訓練出可用於分類和原因分析的決策樹模型。 | varchar | |
decision_tree_predict( decision_tree_model_in_json varchar, input_variable_array array(varchar) ) | 結合decision_tree_classifier函數識別出來的決策樹模型和給定的輸入資料樣本，判別系統對象屬於哪一種分類。 | varchar |
decision_tree_classifier函數
根據最近輸入的一批資料樣本，訓練出可用於分類和原因分析的決策樹模型。
varchar decision_tree_classifier(target_variable varchar,input_variable_array array(varchar),target_variable_name varchar,input_variable_name_array array(varchar),input_variable_type_array array(varchar),<optional> model_options varchar)參數 | 說明 |
| 作為目標變數的欄位。 |
| 輸入變數形成的數組，把輸入變數轉換成為字元類型後組成的一維數組。 |
| 目標變數的名稱。 |
| 輸入變數名稱組成的數組。 |
| 輸入變數類型組成的數組。 輸入變數類型：
|
| 通過索引值對（key=value）組成，多個索引值對使用逗號或分號進行分隔（決策樹模型的進階參數，通常無需設定）。例如： 決策樹模型進階參數：
|
使用樣本
查詢和分析語句
* | with sleep_health_group_data as ( select g.group_id, s.* from ( select 'G1' as group_id union all select 'G2' as group_id ) as g -- 增加一個group_id的欄位用於展示決策樹模型識別是一個彙總函式 cross join log as s ) select group_id, decision_tree_classifier( sleep_disorder, array[cast(person_id as varchar), cast(gender as varchar), cast(age as varchar), cast(occupation as varchar), cast(sleep_duration as varchar), cast(quality_of_sleep as varchar), cast(physical_activity_level as varchar), cast(stress_level as varchar), cast(bmi_category as varchar), cast(blood_pressure_systolic as varchar), cast(blood_pressure_diastolic as varchar), cast(heart_rate as varchar), cast(daily_steps as varchar)], 'sleep_disorder', array['person_id', 'gender', 'age', 'occupation', 'sleep_duration', 'quality_of_sleep', 'physical_activity_level', 'stress_level', 'bmi_category', 'blood_pressure_systolic', 'blood_pressure_diastolic', 'heart_rate', 'daily_steps'], array['ID_NUM', 'X_STR_CATEGORICAL', 'X_NUMERIC', 'X_STR_CATEGORICAL', 'X_NUMERIC', 'X_NUMERIC', 'X_NUMERIC', 'X_NUMERIC', 'X_STR_CATEGORICAL', 'X_NUMERIC', 'X_NUMERIC', 'X_NUMERIC', 'X_NUMERIC'] ) as sleep_health_model from sleep_health_group_data group by group_id order by group_id返回結果
傳回值中的
sleep_health_model是返回的模型，decisionTreeEncode是模型序列化結果。此函數返回的決策樹模型用於decision_tree_predict函數判別該系統對象的分類。group_id
sleep_health_model
G1
{ "returnCode": 0, "message": "OK", "decisionTreeEncode": "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", "decisionTreeInText": "|--- blood_pressure_diastolic \u003c\u003d 93.50\n| |--- bmi_category.Normal \u003c\u003d 0.50\n| | |--- blood_pressure_systolic \u003c\u003d 128.50\n| | | |--- class: None\n| | |--- blood_pressure_systolic \u003e 128.50\n| | | |--- daily_steps \u003c\u003d 5600.00\n| | | | |--- class: Sleep Apnea\n| | | |--- daily_steps \u003e 5600.00\n| | | | |--- class: Insomnia\n| |--- bmi_category.Normal \u003e 0.50\n| | |--- class: None\n|--- blood_pressure_diastolic \u003e 93.50\n| |--- class: Sleep Apnea\n", "uniqueLabels": [ "Insomnia", "None", "Sleep Apnea" ], "confusionMatrix": [ [ 120, 14, 20 ], [ 8, 420, 10 ], [ 2, 10, 144 ] ], "dataColumnNames": [ "person_id", "gender", "age", "occupation", "sleep_duration", "quality_of_sleep", "physical_activity_level", "stress_level", "bmi_category", "blood_pressure_systolic", "blood_pressure_diastolic", "heart_rate", "daily_steps", "sleep_disorder" ], "dataColumnTypes": { "occupation": "X_STR_CATEGORICAL", "blood_pressure_diastolic": "X_NUMERIC", "gender": "X_STR_CATEGORICAL", "heart_rate": "X_NUMERIC", "blood_pressure_systolic": "X_NUMERIC", "stress_level": "X_NUMERIC", "daily_steps": "X_NUMERIC", "physical_activity_level": "X_NUMERIC", "bmi_category": "X_STR_CATEGORICAL", "sleep_duration": "X_NUMERIC", "quality_of_sleep": "X_NUMERIC", "sleep_disorder": "Y_STR_CATEGORICAL", "age": "X_NUMERIC", "person_id": "ID_NUM" }, "categoricalVariableValues": { "bmi_category": [ "Normal", "Normal Weight", "Obese", "Overweight" ], "gender": [ "Female", "Male" ], "occupation": [ "Accountant", "Doctor", "Engineer", "Lawyer", "Manager", "Nurse", "Sales Representative", "Salesperson", "Scientist", "Software Engineer", "Teacher" ] } }G2
{ "returnCode": 0, "message": "OK", "decisionTreeEncode": "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", "decisionTreeInText": "|--- blood_pressure_diastolic \u003c\u003d 93.50\n| |--- bmi_category.Normal \u003c\u003d 0.50\n| | |--- blood_pressure_systolic \u003c\u003d 128.50\n| | | |--- class: None\n| | |--- blood_pressure_systolic \u003e 128.50\n| | | |--- daily_steps \u003c\u003d 5600.00\n| | | | |--- class: Sleep Apnea\n| | | |--- daily_steps \u003e 5600.00\n| | | | |--- class: Insomnia\n| |--- bmi_category.Normal \u003e 0.50\n| | |--- class: None\n|--- blood_pressure_diastolic \u003e 93.50\n| |--- class: Sleep Apnea\n", "uniqueLabels": [ "Insomnia", "None", "Sleep Apnea" ], "confusionMatrix": [ [ 120, 14, 20 ], [ 8, 420, 10 ], [ 2, 10, 144 ] ], "dataColumnNames": [ "person_id", "gender", "age", "occupation", "sleep_duration", "quality_of_sleep", "physical_activity_level", "stress_level", "bmi_category", "blood_pressure_systolic", "blood_pressure_diastolic", "heart_rate", "daily_steps", "sleep_disorder" ], "dataColumnTypes": { "occupation": "X_STR_CATEGORICAL", "blood_pressure_diastolic": "X_NUMERIC", "gender": "X_STR_CATEGORICAL", "heart_rate": "X_NUMERIC", "blood_pressure_systolic": "X_NUMERIC", "stress_level": "X_NUMERIC", "daily_steps": "X_NUMERIC", "physical_activity_level": "X_NUMERIC", "bmi_category": "X_STR_CATEGORICAL", "sleep_duration": "X_NUMERIC", "quality_of_sleep": "X_NUMERIC", "sleep_disorder": "Y_STR_CATEGORICAL", "age": "X_NUMERIC", "person_id": "ID_NUM" }, "categoricalVariableValues": { "bmi_category": [ "Normal", "Normal Weight", "Obese", "Overweight" ], "gender": [ "Female", "Male" ], "occupation": [ "Accountant", "Doctor", "Engineer", "Lawyer", "Manager", "Nurse", "Sales Representative", "Salesperson", "Scientist", "Software Engineer", "Teacher" ] } }
decision_tree_predict函數
結合識別出來的決策樹模型和給定的輸入資料樣本，判別系統對象屬於哪一種分類。
varchar decision_tree_predict(decision_tree_model_in_json varchar,input_variable_array array(varchar))參數 | 說明 |
| |
| 進行判別分類的輸入變數資料（一維數組）。 |
使用樣本
查詢和分析語句
* | with model as ( select 'G1' as group_id, '{"returnCode":0,"message":"OK","decisionTree":{"nodeKey":0,"parentNodeKey":-1,"isLeaf":false,"numSamplesByClass":[124.66666666666676,124.66666666666688,124.66666666666683],"numSamples":374.00000000000045,"probabilitiesByClass":[0.33333333333333315,0.33333333333333354,0.33333333333333337],"predictedClass":"None","predictedClassProbability":0.33333333333333354,"splittingFeature":"blood_pressure_diastolic","threshold":93.5,"depth":1,"leftChild":{"nodeKey":1,"parentNodeKey":0,"isLeaf":false,"numSamplesByClass":[123.04761904761914,121.82039573820417,30.367521367521377],"numSamples":275.2355361533447,"probabilitiesByClass":[0.4470629801925882,0.4426041689265487,0.11033285088086307],"predictedClass":"Insomnia","predictedClassProbability":0.4470629801925882,"splittingFeature":"bmi_category.Normal","threshold":0.5,"depth":2,"leftChild":{"nodeKey":2,"parentNodeKey":1,"isLeaf":false,"numSamplesByClass":[111.7142857142858,17.646879756468795,22.37606837606838],"numSamples":151.737233846823,"probabilitiesByClass":[0.7362351539046778,0.11629894198732474,0.14746590410799743],"predictedClass":"Insomnia","predictedClassProbability":0.7362351539046778,"splittingFeature":"blood_pressure_systolic","threshold":128.5,"depth":3,"leftChild":{"nodeKey":3,"parentNodeKey":2,"isLeaf":true,"numSamplesByClass":[0.0,15.369863013698625,0.0],"numSamples":15.369863013698625,"probabilitiesByClass":[0.0,1.0,0.0],"predictedClass":"None","predictedClassProbability":1.0,"threshold":0.0,"depth":4},"rightChild":{"nodeKey":4,"parentNodeKey":2,"isLeaf":false,"numSamplesByClass":[111.7142857142858,2.2770167427701673,22.37606837606838],"numSamples":136.36737083312434,"probabilitiesByClass":[0.8192156601082596,0.016697665496217574,0.16408667439552274],"predictedClass":"Insomnia","predictedClassProbability":0.8192156601082596,"splittingFeature":"daily_steps","threshold":5600.0,"depth":4,"leftChild":{"nodeKey":5,"parentNodeKey":4,"isLeaf":true,"numSamplesByClass":[14.57142857142857,0.0,20.77777777777778],"numSamples":35.34920634920635,"probabilitiesByClass":[0.41221374045801523,0.0,0.5877862595419848],"predictedClass":"Sleep Apnea","predictedClassProbability":0.5877862595419848,"threshold":0.0,"depth":5},"rightChild":{"nodeKey":6,"parentNodeKey":4,"isLeaf":true,"numSamplesByClass":[97.14285714285721,2.2770167427701673,1.5982905982905984],"numSamples":101.01816448391799,"probabilitiesByClass":[0.9616375197385643,0.022540666368301186,0.015821813893134487],"predictedClass":"Insomnia","predictedClassProbability":0.9616375197385643,"threshold":0.0,"depth":5}}},"rightChild":{"nodeKey":7,"parentNodeKey":1,"isLeaf":true,"numSamplesByClass":[11.333333333333332,104.17351598173533,7.9914529914529915],"numSamples":123.49830230652165,"probabilitiesByClass":[0.09176914274662742,0.8435218463422892,0.06470901091108344],"predictedClass":"None","predictedClassProbability":0.8435218463422892,"threshold":0.0,"depth":3}},"rightChild":{"nodeKey":8,"parentNode Key":0,"isLeaf":true,"numSamplesByClass":[1.619047619047619,2.846270928462709,94.29914529914537],"numSamples":98.7644638466557,"probabilitiesByClass":[0.016393017852670114,0.028818775677068465,0.9547882064702613],"predictedClass":"Sleep Apnea","predictedClassProbability":0.9547882064702613,"threshold":0.0,"depth":2}},"decisionTreeClassLabels":["Insomnia","None","Sleep Apnea"],"decisionTreeEncode":"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\u003d","decisionTreeInText":"|--- blood_pressure_diastolic \u003c\u003d 93.50\n| |--- bmi_category.Normal \u003c\u003d 0.50\n| | |--- blood_pressure_systolic \u003c\u003d 128.50\n| | | |--- class: None\n| | |--- blood_pressure_systolic \u003e 128.50\n| | | |--- daily_steps \u003c\u003d 5600.00\n| | | | |--- class: Sleep Apnea\n| | | |--- daily_steps \u003e 5600.00\n| | | | |--- class: Insomnia\n| |--- bmi_category.Normal \u003e 0.50\n| | |--- class: None\n|--- blood_pressure_diastolic \u003e 93.50\n| |--- class: Sleep Apnea\n","uniqueLabels":["Insomnia","None","Sleep Apnea"],"confusionMatrix":[[60,7,10],[4,210,5],[1,5,72]],"dataColumnNames":["person_id","gender","age","occupation","sleep_duration","quality_of_sleep","physical_activity_level","stress_level","bmi_category","blood_pressure_systolic","blood_pressure_diastolic","heart_rate","daily_steps","sleep_disorder"],"expandedColumnNames":["gender.Female","age","occupation.Accountant","occupation.Doctor","occupation.Engineer","occupation.Lawyer","occupation.Manager","occupation.Nurse","occupation.Sales Representative","occupation.Salesperson","occupation.Scientist","occupation.Software Engineer","sleep_duration","quality_of_sleep","physical_activity_level","stress_level","bmi_category.Normal","bmi_category.Normal Weight","bmi_category.Obese","blood_pressure_systolic","blood_pressure_diastolic","heart_rate","daily_steps"],"dataColumnTypes":{"occupation":"X_STR_CATEGORICAL","blood_pressure_diastolic":"X_NUMERIC","gender":"X_STR_CATEGORICAL","heart_rate":"X_NUMERIC","blood_pressure_systolic":"X_NUMERIC","stress_level":"X_NUMERIC","daily_steps":"X_NUMERIC","physical_activity_level":"X_NUMERIC","bmi_category":"X_STR_CATEGORICAL","sleep_duration":"X_NUMERIC","quality_of_sleep":"X_NUMERIC","sleep_disorder":"Y_STR_CATEGORICAL","age":"X_NUMERIC","person_id":"ID_NUM"},"categoricalVariableValues":{"bmi_category":["Normal","Normal Weight","Obese","Overweight"],"gender":["Female","Male"],"occupation":["Accountant","Doctor","Engineer","Lawyer","Manager","Nurse","Sales Representative","Salesperson","Scientist","Software Engineer","Teacher"]},"modelVersion":"1.0.0-20230821"}' as decision_tree_model, count(*) as record_count from log ), sleep_health_data as ( select 1 as person_id, 'Male' as gender, 27 as age, 'Software Engineer' as occupation, 6.1 as sleep_duration, 6 as quality_of_sleep, 42 as physical_activity_level, 6 as stress_level, 'Overweight' as bmi_category, 126 as blood_pressure_systolic, 83 as blood_pressure_diastolic, 77 as heart_rate, 4200 as daily_steps, 'None' as sleep_disorder union all select 2 as person_id, 'Male' as gender, 28 as age, 'Doctor' as occupation, 6.2 as sleep_duration, 6 as quality_of_sleep, 60 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 125 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 75 as heart_rate, 10000 as daily_steps, 'None' as sleep_disorder union all select 3 as person_id, 'Male' as gender, 28 as age, 'Doctor' as occupation, 6.2 as sleep_duration, 6 as quality_of_sleep, 60 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 125 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 75 as heart_rate, 10000 as daily_steps, 'None' as sleep_disorder union all select 4 as person_id, 'Male' as gender, 28 as age, 'Sales Representative' as occupation, 5.9 as sleep_duration, 4 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Obese' as bmi_category, 140 as blood_pressure_systolic, 90 as blood_pressure_diastolic, 85 as heart_rate, 3000 as daily_steps, 'Sleep Apnea' as sleep_disorder union all select 5 as person_id, 'Male' as gender, 28 as age, 'Sales Representative' as occupation, 5.9 as sleep_duration, 4 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Obese' as bmi_category, 140 as blood_pressure_systolic, 90 as blood_pressure_diastolic, 85 as heart_rate, 3000 as daily_steps, 'Sleep Apnea' as sleep_disorder union all select 6 as person_id, 'Male' as gender, 28 as age, 'Software Engineer' as occupation, 5.9 as sleep_duration, 4 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Obese' as bmi_category, 140 as blood_pressure_systolic, 90 as blood_pressure_diastolic, 85 as heart_rate, 3000 as daily_steps, 'Insomnia' as sleep_disorder union all select 7 as person_id, 'Male' as gender, 29 as age, 'Teacher' as occupation, 6.3 as sleep_duration, 6 as quality_of_sleep, 40 as physical_activity_level, 7 as stress_level, 'Obese' as bmi_category, 140 as blood_pressure_systolic, 90 as blood_pressure_diastolic, 82 as heart_rate, 3500 as daily_steps, 'Insomnia' as sleep_disorder union all select 8 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 7.8 as sleep_duration, 7 as quality_of_sleep, 75 as physical_activity_level, 6 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder union all select 9 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 7.8 as sleep_duration, 7 as quality_of_sleep, 75 as physical_activity_level, 6 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder union all select 10 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 7.8 as sleep_duration, 7 as quality_of_sleep, 75 as physical_activity_level, 6 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder union all select 11 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 6.1 as sleep_duration, 6 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder union all select 12 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 7.8 as sleep_duration, 7 as quality_of_sleep, 75 as physical_activity_level, 6 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder union all select 13 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 6.1 as sleep_duration, 6 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder union all select 14 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 6 as sleep_duration, 6 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder union all select 15 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 6 as sleep_duration, 6 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder union all select 16 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 6 as sleep_duration, 6 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder union all select 17 as person_id, 'Female' as gender, 29 as age, 'Nurse' as occupation, 6.5 as sleep_duration, 5 as quality_of_sleep, 40 as physical_activity_level, 7 as stress_level, 'Normal Weight' as bmi_category, 132 as blood_pressure_systolic, 87 as blood_pressure_diastolic, 80 as heart_rate, 4000 as daily_steps, 'Sleep Apnea' as sleep_disorder union all select 18 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 6 as sleep_duration, 6 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'Sleep Apnea' as sleep_disorder union all select 19 as person_id, 'Female' as gender, 29 as age, 'Nurse' as occupation, 6.5 as sleep_duration, 5 as quality_of_sleep, 40 as physical_activity_level, 7 as stress_level, 'Normal Weight' as bmi_category, 132 as blood_pressure_systolic, 87 as blood_pressure_diastolic, 80 as heart_rate, 4000 as daily_steps, 'Insomnia' as sleep_disorder union all select 20 as person_id, 'Male' as gender, 30 as age, 'Doctor' as occupation, 7.6 as sleep_duration, 7 as quality_of_sleep, 75 as physical_activity_level, 6 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder ) select gm.group_id, nid.person_id, decision_tree_predict( gm.decision_tree_model, array[cast(person_id as varchar), cast(gender as varchar), cast(age as varchar), cast(occupation as varchar), cast(sleep_duration as varchar), cast(quality_of_sleep as varchar), cast(physical_activity_level as varchar), cast(stress_level as varchar), cast(bmi_category as varchar), cast(blood_pressure_systolic as varchar), cast(blood_pressure_diastolic as varchar), cast(heart_rate as varchar), cast(daily_steps as varchar)]) as predicted_value from model as gm cross join sleep_health_data as nid order by gm.group_id, nid.person_id limit 10000返回結果
傳回值中的
predicted_value是輸入範例input_variable_array所屬的分類。group_id
person_id
predicted_value
G1
4
Sleep Apnea
G1
5
Sleep Apnea
G1
6
Sleep Apnea
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