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    Simple Log Service:Classification analysis functions

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    Simple Log Service:Classification analysis functions

    Last Updated:Jun 22, 2026

    Classification machine learning models identify the classes of system objects in real time, such as detecting attack requests or uncovering relationships between elements. The following sections describe the syntax and examples of classification analysis functions.

    Background information

    • The following sample index is used in the classification function examples. For more information, see Create indexes.

      The sample index contains the following 14 fields: person_id (long, patient ID), gender (text, gender), age (long, age), occupation (text, occupation), sleep_duration (double, sleep duration), quality_of_sleep (long, quality of sleep), physical_activity_level (long, physical activity level), stress_level (long, stress level), bmi_category (text, BMI category), blood_pressure_systolic (long, systolic blood pressure), blood_pressure_diastolic (long, diastolic blood pressure), heart_rate (long, heart rate), daily_steps (long, daily steps), and sleep_disorder (text, sleep disorder). The statistics feature is enabled for all fields.

    • The following code shows the sample log:

      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

    Functions

    Classification analysis functions use a machine learning model to determine the class of a system object in real time.

    Function

    Syntax

    Description

    Data type of the return value

    decision_tree_classifier function

    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

    )

    Returns a trained decision tree model for data classification and cause analysis based on the specified samples.

    varchar

    decision_tree_predict function

    decision_tree_predict(

    decision_tree_model_in_json varchar,

    input_variable_array array(varchar)

    )

    Identifies the classes of system objects based on the specified samples and the decision tree model returned by the decision_tree_classifier function.

    varchar

    decision_tree_classifier function

    The decision_tree_classifier function returns a trained decision tree model for data classification and cause analysis based on the specified samples.

    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)

    Parameter

    Description

    target_variable

    The output variable.

    input_variable_array

    An array of the input variables. Each variable must be converted to a string.

    target_variable_name

    The name of the output variable.

    input_variable_name_array

    The array of input variable names.

    input_variable_type_array

    The array of input variable types.

    Supported input variable types:

    • ID_STR: the ID of the string type for the classification object.

    • ID_NUM: the ID of the numeric type for the classification object.

    • X_STR_CATEGORICAL: the discrete data of the string type. For example, the valid values of a field are Male, Female, and Unknown.

    • X_NUM_CATEGORICAL: the discrete data of the numeric type. For example, the valid values of a field are 1, 2, 3, 4, and 5.

    • X_NUMERIC: the continuous data of the numeric type.

    model_options

    Optional. Advanced parameters specified as a comma- or semicolon-separated string of key-value pairs. This parameter is typically not required. For example: criterion=gini,splitter=best,max_depth=100,min_samples_split=20,min_samples_leaf=10,min_weight_fraction_leaf=0.01,max_leaf_nodes=100000,min_impurity_decrease=0.01,class_weight=balanced.

    Advanced parameters for the decision tree model:

    • criterion: the criteria for node splitting. Set the value to gini, entropy, or log_loss.

    • splitter: the policy used to split a node. Set the value to best or random.

    • max_depth: the maximum depth of the decision tree.

    • min_samples_split: the minimum number of samples that are required to split a node.

    • min_samples_leaf: The minimum number of samples required at a leaf node.

    • min_weight_fraction_leaf: the minimum proportion of the sample weight to the total sample weight.

    • max_leaf_nodes: the maximum number of leaf nodes.

    • min_impurity_decrease: the impurity decrease that is required to split a node.

    • class_weight: the weight of the class. Setting this to balanced addresses sample imbalance by making sample weights inversely proportional to the number of samples in each class. The more samples a class has, the lower the weight per sample.

    Example

    • Query statement

      * | 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         -- Add a group_id field to show that decision_tree_classifier acts as an aggregate function.
  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

    • Query and analysis results

      The sleep_health_model field contains the decision tree model. The decisionTreeEncode field contains the serialized decision tree model. You can use this model in the decision_tree_predict function to identify the class of a system object.

      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 function

    The decision_tree_predict function identifies the classes of system objects based on the specified samples and the returned decision tree model.

    varchar decision_tree_predict(decision_tree_model_in_json varchar,input_variable_array array(varchar))

    Parameter

    Description

    decision_tree_model_in_json

    The return value of the decision_tree_classifier function.

    input_variable_array

    The array of input variables used for classification. The function converts the input variables into a one-dimensional array.

    Example

    • Query statement

      * | 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

    • Query and analysis results

      The predicted_value field indicates the class that the input variables in input_variable_array belong to.

      group_id

      person_id

      predicted_value

      G1

      4

      Sleep Apnea

      G1

      5

      Sleep Apnea

      G1

      6

      Sleep Apnea

      ...

      ...

      ...