The classification model supported by Simple Log Service SQL functions - Simple Log Service

Classification is a machine learning model that you can use to identify the classes of system objects online. For example, you can use the model to identify attack requests. You can also use the model to identify the relationships between elements. This topic describes the syntax of classification analysis functions. This topic also provides examples on how to use the functions.

Background information

The following figure shows sample indexes for the classification analysis functions. For more information, see Create indexes.

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

You can use the machine learning model of classification to identify the classes of system objects online.

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 that can be used for data classification and cause analysis based on the recently 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 that is returned by the decision_tree_classifier function.

varchar

decision_tree_classifier function

The decision_tree_classifier function returns a trained decision tree model that can be used for data classification and cause analysis based on the recently 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`	The array of input variables. The function converts the input variables to the string type and forms a one-dimensional array.
`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`	The advanced parameters of the decision tree model. In most cases, you do not need to configure this parameter. The value consists of key-value pairs. Multiple key-value pairs are separated with commas (,) or semicolons (:). For example, you can specify `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 of 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 in 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. If you set the parameter to `balanced` when you handle the sample imbalance issue, the weight of samples is inversely proportional to the number of samples of various types. In this case, the larger the number of samples, the lower the weight of a 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 the group_id field to specify that an aggregate function is returned in the decision tree model-based identification.
  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 indicates the decision tree model. The decisionTreeEncode field indicates the serialization results of the decision tree model. This function returns a decision tree model that you can use in the decision_tree_predict function to identify the class of a system object.

group_id

sleep_health_model

{
  "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"
    ]
  }
}

{
  "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 decision tree model that is returned.

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 that are used in classification. The function converts the input variables and forms 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 to which the input variables specified by the input_variable_array parameter belong.
group_id
person_id
predicted_value
G1
4
Sleep Apnea
G1
5
Sleep Apnea
G1
6
Sleep Apnea
...
...
...

group_id	person_id	predicted_value
G1	4	Sleep Apnea
G1	5	Sleep Apnea
G1	6	Sleep Apnea
...	...	...