Create data frame analytics jobs API | Elasticsearch Guide [8.13]

Request

PUT _ml/data_frame/analytics/<data_frame_analytics_id>

Prerequisites

Requires the following privileges:

cluster: manage_ml (the machine_learning_admin built-in role grants this privilege)
source indices: read, view_index_metadata
destination index: read, create_index, manage and index

The data frame analytics job remembers which roles the user who created it had at the time of creation. When you start the job, it performs the analysis using those same roles. If you provide secondary authorization headers, those credentials are used instead.

Description

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This API creates a data frame analytics job that performs an analysis on the source indices and stores the outcome in a destination index.

If the destination index does not exist, it is created automatically when you start the job. See Start data frame analytics jobs.

If you supply only a subset of the regression or classification parameters, hyperparameter optimization occurs. It determines a value for each of the undefined parameters.

Path parameters

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<data_frame_analytics_id>: (Required, string) Identifier for the data frame analytics job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

Request body

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allow_lazy_start: (Optional, Boolean) Specifies whether this job can start when there is insufficient machine learning node capacity for it to be immediately assigned to a node. The default is false; if a machine learning node with capacity to run the job cannot immediately be found, the API returns an error. However, this is also subject to the cluster-wide xpack.ml.max_lazy_ml_nodes setting. See Advanced machine learning settings. If this option is set to true, the API does not return an error and the job waits in the starting state until sufficient machine learning node capacity is available.

analysis

(Required, object) The analysis configuration, which contains the information necessary to perform one of the following types of analysis: classification, outlier detection, or regression.

Properties of analysis

classification

(Required^*, object) The configuration information necessary to perform classification.

Advanced parameters are for fine-tuning classification analysis. They are set automatically by hyperparameter optimization to give the minimum validation error. It is highly recommended to use the default values unless you fully understand the function of these parameters.

Properties of classification

alpha

(Optional, double) Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This parameter affects loss calculations by acting as a multiplier of the tree depth. Higher alpha values result in shallower trees and faster training times. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to zero.

class_assignment_objective

(Optional, string) Defines the objective to optimize when assigning class labels: maximize_accuracy or maximize_minimum_recall. When maximizing accuracy, class labels are chosen to maximize the number of correct predictions. When maximizing minimum recall, labels are chosen to maximize the minimum recall for any class. Defaults to maximize_minimum_recall.

dependent_variable

(Required, string)

Defines which field of the document is to be predicted. This parameter is supplied by field name and must match one of the fields in the index being used to train. If this field is missing from a document, then that document will not be used for training, but a prediction with the trained model will be generated for it. It is also known as continuous target variable.

The data type of the field must be numeric (integer, short, long, byte), categorical (ip or keyword), or boolean. There must be no more than 100 different values in this field.

downsample_factor

(Optional, double) Advanced configuration option. Controls the fraction of data that is used to compute the derivatives of the loss function for tree training. A small value results in the use of a small fraction of the data. If this value is set to be less than 1, accuracy typically improves. However, too small a value may result in poor convergence for the ensemble and so require more trees. For more information about shrinkage, refer to this wiki article. By default, this value is calculated during hyperparameter optimization. It must be greater than zero and less than or equal to 1.

early_stopping_enabled

(Optional, Boolean) Advanced configuration option. Specifies whether the training process should finish if it is not finding any better performing models. If disabled, the training process can take significantly longer and the chance of finding a better performing model is unremarkable. By default, early stoppping is enabled.

eta

(Optional, double) Advanced configuration option. The shrinkage applied to the weights. Smaller values result in larger forests which have a better generalization error. However, larger forests cause slower training. For more information about shrinkage, refer to this wiki article. By default, this value is calculated during hyperparameter optimization. It must be a value between 0.001 and 1.

eta_growth_rate_per_tree

(Optional, double) Advanced configuration option. Specifies the rate at which eta increases for each new tree that is added to the forest. For example, a rate of 1.05 increases eta by 5% for each extra tree. By default, this value is calculated during hyperparameter optimization. It must be between 0.5 and 2.

feature_bag_fraction

(Optional, double) Advanced configuration option. Defines the fraction of features that will be used when selecting a random bag for each candidate split. By default, this value is calculated during hyperparameter optimization.

feature_processors

(Optional, list) Advanced configuration option. A collection of feature preprocessors that modify one or more included fields. The analysis uses the resulting one or more features instead of the original document field. However, these features are ephemeral; they are not stored in the destination index. Multiple feature_processors entries can refer to the same document fields. Automatic categorical feature encoding still occurs for the fields that are unprocessed by a custom processor or that have categorical values. Use this property only if you want to override the automatic feature encoding of the specified fields. Refer to data frame analytics feature processors to learn more.

Properties of feature_processors

frequency_encoding

(object) The configuration information necessary to perform frequency encoding.

Properties of frequency_encoding

feature_name: (Required, string) The resulting feature name.
field: (Required, string) The name of the field to encode.
frequency_map: (Required, object) The resulting frequency map for the field value. If the field value is missing from the frequency_map, the resulting value is 0.

multi_encoding

(object) The configuration information necessary to perform multi encoding. It allows multiple processors to be changed together. This way the output of a processor can then be passed to another as an input.

Properties of multi_encoding

processors: (Required, array) The ordered array of custom processors to execute. Must be more than 1.

n_gram_encoding

(object) The configuration information necessary to perform n-gram encoding. Features created by this encoder have the following name format: <feature_prefix>.<ngram><string position>. For example, if the feature_prefix is f, the feature name for the second unigram in a string is f.11.

Properties of n_gram_encoding

feature_prefix: (Optional, string) The feature name prefix. Defaults to ngram_<start>_<length>.
field: (Required, string) The name of the text field to encode.
length: (Optional, integer) Specifies the length of the n-gram substring. Defaults to 50. Must be greater than 0.
n_grams: (Required, array) Specifies which n-grams to gather. It’s an array of integer values where the minimum value is 1, and a maximum value is 5.
start: (Optional, integer) Specifies the zero-indexed start of the n-gram substring. Negative values are allowed for encoding n-grams of string suffixes. Defaults to 0.

one_hot_encoding

(object) The configuration information necessary to perform one hot encoding.

Properties of one_hot_encoding

field: (Required, string) The name of the field to encode.
hot_map: (Required, string) The one hot map mapping the field value with the column name.

target_mean_encoding

(object) The configuration information necessary to perform target mean encoding.

Properties of target_mean_encoding

default_value: (Required, integer) The default value if field value is not found in the target_map.
feature_name: (Required, string) The resulting feature name.
field: (Required, string) The name of the field to encode.
target_map: (Required, object) The field value to target mean transition map.

gamma

(Optional, double) Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies a linear penalty associated with the size of individual trees in the forest. A high gamma value causes training to prefer small trees. A small gamma value results in larger individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.

lambda

(Optional, double) Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies an L2 regularization term which applies to leaf weights of the individual trees in the forest. A high lambda value causes training to favor small leaf weights. This behavior makes the prediction function smoother at the expense of potentially not being able to capture relevant relationships between the features and the dependent variable. A small lambda value results in large individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.

max_optimization_rounds_per_hyperparameter

(Optional, integer) Advanced configuration option. A multiplier responsible for determining the maximum number of hyperparameter optimization steps in the Bayesian optimization procedure. The maximum number of steps is determined based on the number of undefined hyperparameters times the maximum optimization rounds per hyperparameter. By default, this value is calculated during hyperparameter optimization.

max_trees

(Optional, integer) Advanced configuration option. Defines the maximum number of decision trees in the forest. The maximum value is 2000. By default, this value is calculated during hyperparameter optimization.

num_top_classes

(Optional, integer) Defines the number of categories for which the predicted probabilities are reported. It must be non-negative or -1. If it is -1 or greater than the total number of categories, probabilities are reported for all categories; if you have a large number of categories, there could be a significant effect on the size of your destination index. Defaults to 2.

To use the AUC ROC evaluation method, num_top_classes must be set to -1 or a value greater than or equal to the total number of categories.

num_top_feature_importance_values

(Optional, integer) Advanced configuration option. Specifies the maximum number of feature importance values per document to return. By default, it is zero and no feature importance calculation occurs.

prediction_field_name

(Optional, string) Defines the name of the prediction field in the results. Defaults to <dependent_variable>_prediction.

randomize_seed

(Optional, long) Defines the seed for the random generator that is used to pick training data. By default, it is randomly generated. Set it to a specific value to use the same training data each time you start a job (assuming other related parameters such as source and analyzed_fields are the same).

soft_tree_depth_limit

(Optional, double) Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This soft limit combines with the soft_tree_depth_tolerance to penalize trees that exceed the specified depth; the regularized loss increases quickly beyond this depth. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.

soft_tree_depth_tolerance

(Optional, double) Advanced configuration option. This option controls how quickly the regularized loss increases when the tree depth exceeds soft_tree_depth_limit. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.01.

training_percent

(Optional, integer) Defines what percentage of the eligible documents that will be used for training. Documents that are ignored by the analysis (for example those that contain arrays with more than one value) won’t be included in the calculation for used percentage. Defaults to 100.

outlier_detection

(Required^*, object) The configuration information necessary to perform outlier detection:

Properties of outlier_detection

compute_feature_influence: (Optional, Boolean) Specifies whether the feature influence calculation is enabled. Defaults to true.
feature_influence_threshold: (Optional, double) The minimum outlier score that a document needs to have in order to calculate its feature influence score. Value range: 0-1 (0.1 by default).
method: (Optional, string) The method that outlier detection uses. Available methods are lof, ldof, distance_kth_nn, distance_knn, and ensemble. The default value is ensemble, which means that outlier detection uses an ensemble of different methods and normalises and combines their individual outlier scores to obtain the overall outlier score.
n_neighbors: (Optional, integer) Defines the value for how many nearest neighbors each method of outlier detection uses to calculate its outlier score. When the value is not set, different values are used for different ensemble members. This default behavior helps improve the diversity in the ensemble; only override it if you are confident that the value you choose is appropriate for the data set.
outlier_fraction: (Optional, double) The proportion of the data set that is assumed to be outlying prior to outlier detection. For example, 0.05 means it is assumed that 5% of values are real outliers and 95% are inliers.
standardization_enabled: (Optional, Boolean) If true, the following operation is performed on the columns before computing outlier scores: (x_i - mean(x_i)) / sd(x_i). Defaults to true. For more information about this concept, see Wikipedia.

regression

(Required^*, object) The configuration information necessary to perform regression.

Advanced parameters are for fine-tuning regression analysis. They are set automatically by hyperparameter optimization to give the minimum validation error. It is highly recommended to use the default values unless you fully understand the function of these parameters.

Properties of regression

alpha

(Optional, double) Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This parameter affects loss calculations by acting as a multiplier of the tree depth. Higher alpha values result in shallower trees and faster training times. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to zero.

dependent_variable

(Required, string)

Defines which field of the document is to be predicted. This parameter is supplied by field name and must match one of the fields in the index being used to train. If this field is missing from a document, then that document will not be used for training, but a prediction with the trained model will be generated for it. It is also known as continuous target variable.

The data type of the field must be numeric.

downsample_factor

(Optional, double) Advanced configuration option. Controls the fraction of data that is used to compute the derivatives of the loss function for tree training. A small value results in the use of a small fraction of the data. If this value is set to be less than 1, accuracy typically improves. However, too small a value may result in poor convergence for the ensemble and so require more trees. For more information about shrinkage, refer to this wiki article. By default, this value is calculated during hyperparameter optimization. It must be greater than zero and less than or equal to 1.

early_stopping_enabled

(Optional, Boolean) Advanced configuration option. Specifies whether the training process should finish if it is not finding any better performing models. If disabled, the training process can take significantly longer and the chance of finding a better performing model is unremarkable. By default, early stoppping is enabled.

eta

(Optional, double) Advanced configuration option. The shrinkage applied to the weights. Smaller values result in larger forests which have a better generalization error. However, larger forests cause slower training. For more information about shrinkage, refer to this wiki article. By default, this value is calculated during hyperparameter optimization. It must be a value between 0.001 and 1.

eta_growth_rate_per_tree

(Optional, double) Advanced configuration option. Specifies the rate at which eta increases for each new tree that is added to the forest. For example, a rate of 1.05 increases eta by 5% for each extra tree. By default, this value is calculated during hyperparameter optimization. It must be between 0.5 and 2.

feature_bag_fraction

(Optional, double) Advanced configuration option. Defines the fraction of features that will be used when selecting a random bag for each candidate split. By default, this value is calculated during hyperparameter optimization.

feature_processors

(Optional, list) Advanced configuration option. A collection of feature preprocessors that modify one or more included fields. The analysis uses the resulting one or more features instead of the original document field. However, these features are ephemeral; they are not stored in the destination index. Multiple feature_processors entries can refer to the same document fields. Automatic categorical feature encoding still occurs for the fields that are unprocessed by a custom processor or that have categorical values. Use this property only if you want to override the automatic feature encoding of the specified fields. Refer to data frame analytics feature processors to learn more.

gamma

(Optional, double) Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies a linear penalty associated with the size of individual trees in the forest. A high gamma value causes training to prefer small trees. A small gamma value results in larger individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.

lambda

(Optional, double) Advanced configuration option. Regularization parameter to prevent overfitting on the training data set. Multiplies an L2 regularization term which applies to leaf weights of the individual trees in the forest. A high lambda value causes training to favor small leaf weights. This behavior makes the prediction function smoother at the expense of potentially not being able to capture relevant relationships between the features and the dependent variable. A small lambda value results in large individual trees and slower training. By default, this value is calculated during hyperparameter optimization. It must be a nonnegative value.

loss_function

(Optional, string) The loss function used during regression. Available options are mse (mean squared error), msle (mean squared logarithmic error), huber (Pseudo-Huber loss). Defaults to mse. Refer to Loss functions for regression analyses to learn more.

loss_function_parameter

(Optional, double) A positive number that is used as a parameter to the loss_function.

max_optimization_rounds_per_hyperparameter

(Optional, integer) Advanced configuration option. A multiplier responsible for determining the maximum number of hyperparameter optimization steps in the Bayesian optimization procedure. The maximum number of steps is determined based on the number of undefined hyperparameters times the maximum optimization rounds per hyperparameter. By default, this value is calculated during hyperparameter optimization.

max_trees

(Optional, integer) Advanced configuration option. Defines the maximum number of decision trees in the forest. The maximum value is 2000. By default, this value is calculated during hyperparameter optimization.

num_top_feature_importance_values

(Optional, integer) Advanced configuration option. Specifies the maximum number of feature importance values per document to return. By default, it is zero and no feature importance calculation occurs.

prediction_field_name

(Optional, string) Defines the name of the prediction field in the results. Defaults to <dependent_variable>_prediction.

randomize_seed

(Optional, long) Defines the seed for the random generator that is used to pick training data. By default, it is randomly generated. Set it to a specific value to use the same training data each time you start a job (assuming other related parameters such as source and analyzed_fields are the same).

soft_tree_depth_limit

(Optional, double) Advanced configuration option. Machine learning uses loss guided tree growing, which means that the decision trees grow where the regularized loss decreases most quickly. This soft limit combines with the soft_tree_depth_tolerance to penalize trees that exceed the specified depth; the regularized loss increases quickly beyond this depth. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.

soft_tree_depth_tolerance

(Optional, double) Advanced configuration option. This option controls how quickly the regularized loss increases when the tree depth exceeds soft_tree_depth_limit. By default, this value is calculated during hyperparameter optimization. It must be greater than or equal to 0.01.

training_percent

(Optional, integer) Defines what percentage of the eligible documents that will be used for training. Documents that are ignored by the analysis (for example those that contain arrays with more than one value) won’t be included in the calculation for used percentage. Defaults to 100.

analyzed_fields

(Optional, object) Specify includes and/or excludes patterns to select which fields will be included in the analysis. The patterns specified in excludes are applied last, therefore excludes takes precedence. In other words, if the same field is specified in both includes and excludes, then the field will not be included in the analysis.

The supported fields for each type of analysis are as follows:

Outlier detection requires numeric or boolean data to analyze. The algorithms don’t support missing values therefore fields that have data types other than numeric or boolean are ignored. Documents where included fields contain missing values, null values, or an array are also ignored. Therefore the dest index may contain documents that don’t have an outlier score.
Regression supports fields that are numeric, boolean, text, keyword, and ip. It is also tolerant of missing values. Fields that are supported are included in the analysis, other fields are ignored. Documents where included fields contain an array with two or more values are also ignored. Documents in the dest index that don’t contain a results field are not included in the regression analysis.
Classification supports fields that are numeric, boolean, text, keyword, and ip. It is also tolerant of missing values. Fields that are supported are included in the analysis, other fields are ignored. Documents where included fields contain an array with two or more values are also ignored. Documents in the dest index that don’t contain a results field are not included in the classification analysis. Classification analysis can be improved by mapping ordinal variable values to a single number. For example, in case of age ranges, you can model the values as "0-14" = 0, "15-24" = 1, "25-34" = 2, and so on.

If analyzed_fields is not set, only the relevant fields will be included. For example, all the numeric fields for outlier detection. For more information about field selection, see Explain data frame analytics.

Properties of analyzed_fields

excludes: (Optional, array) An array of strings that defines the fields that will be excluded from the analysis. You do not need to add fields with unsupported data types to excludes, these fields are excluded from the analysis automatically.
includes: (Optional, array) An array of strings that defines the fields that will be included in the analysis.

description

(Optional, string) A description of the job.

dest

(Required, object) The destination configuration, consisting of index and optionally results_field (ml by default).

Properties of dest

index: (Required, string) Defines the destination index to store the results of the data frame analytics job.
results_field: (Optional, string) Defines the name of the field in which to store the results of the analysis. Defaults to ml.

max_num_threads

(Optional, integer) The maximum number of threads to be used by the analysis. The default value is 1. Using more threads may decrease the time necessary to complete the analysis at the cost of using more CPU. Note that the process may use additional threads for operational functionality other than the analysis itself.

_meta

(Optional, object) Advanced configuration option. Contains custom metadata about the job. For example, it can contain custom URL information.

model_memory_limit

(Optional, string) The approximate maximum amount of memory resources that are permitted for analytical processing. The default value for data frame analytics jobs is 1gb. If you specify a value for the xpack.ml.max_model_memory_limit setting, an error occurs when you try to create jobs that have model_memory_limit values greater than that setting value. For more information, see Machine learning settings.

source

(object) The configuration of how to source the analysis data. It requires an index. Optionally, query, runtime_mappings, and _source may be specified.

Properties of source

index

(Required, string or array) Index or indices on which to perform the analysis. It can be a single index or index pattern as well as an array of indices or patterns.

If your source indices contain documents with the same IDs, only the document that is indexed last appears in the destination index.

query

(Optional, object) The Elasticsearch query domain-specific language (DSL). This value corresponds to the query object in an Elasticsearch search POST body. All the options that are supported by Elasticsearch can be used, as this object is passed verbatim to Elasticsearch. By default, this property has the following value: {"match_all": {}}.

runtime_mappings

(Optional, object) Definitions of runtime fields that will become part of the mapping of the destination index.

_source

(Optional, object) Specify includes and/or excludes patterns to select which fields will be present in the destination. Fields that are excluded cannot be included in the analysis.

Properties of _source

includes: (array) An array of strings that defines the fields that will be included in the destination.
excludes: (array) An array of strings that defines the fields that will be excluded from the destination.

Examples

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Preprocessing actions example

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The following example shows how to limit the scope of the analysis to certain fields, specify excluded fields in the destination index, and use a query to filter your data before analysis.

PUT _ml/data_frame/analytics/model-flight-delays-pre
{
  "source": {
    "index": [
      "kibana_sample_data_flights" 
    ],
    "query": { 
      "range": {
        "DistanceKilometers": {
          "gt": 0
        }
      }
    },
    "_source": { 
      "includes": [],
      "excludes": [
        "FlightDelay",
        "FlightDelayType"
      ]
    }
  },
  "dest": { 
    "index": "df-flight-delays",
    "results_field": "ml-results"
  },
  "analysis": {
  "regression": {
    "dependent_variable": "FlightDelayMin",
    "training_percent": 90
    }
  },
  "analyzed_fields": { 
    "includes": [],
    "excludes": [
      "FlightNum"
    ]
  },
  "model_memory_limit": "100mb"
}

	Source index to analyze.
	This query filters out entire documents that will not be present in the destination index.
	The `_source` object defines fields in the data set that will be included or excluded in the destination index.
	Defines the destination index that contains the results of the analysis and the fields of the source index specified in the `_source` object. Also defines the name of the `results_field`.
	Specifies fields to be included in or excluded from the analysis. This does not affect whether the fields will be present in the destination index, only affects whether they are used in the analysis.

In this example, we can see that all the fields of the source index are included in the destination index except FlightDelay and FlightDelayType because these are defined as excluded fields by the excludes parameter of the _source object. The FlightNum field is included in the destination index, however it is not included in the analysis because it is explicitly specified as excluded field by the excludes parameter of the analyzed_fields object.

Outlier detection example

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The following example creates the loganalytics data frame analytics job, the analysis type is outlier_detection:

PUT _ml/data_frame/analytics/loganalytics
{
  "description": "Outlier detection on log data",
  "source": {
    "index": "logdata"
  },
  "dest": {
    "index": "logdata_out"
  },
  "analysis": {
    "outlier_detection": {
      "compute_feature_influence": true,
      "outlier_fraction": 0.05,
      "standardization_enabled": true
    }
  }
}

The API returns the following result:

{
  "id" : "loganalytics",
  "create_time" : 1656364565517,
  "version" : "8.4.0",
  "authorization" : {
    "roles" : [
      "superuser"
    ]
  },
  "description" : "Outlier detection on log data",
  "source" : {
    "index" : [
      "logdata"
    ],
    "query" : {
      "match_all" : { }
    }
  },
  "dest" : {
    "index" : "logdata_out",
    "results_field" : "ml"
  },
  "analysis" : {
    "outlier_detection" : {
      "compute_feature_influence" : true,
      "outlier_fraction" : 0.05,
      "standardization_enabled" : true
    }
  },
  "model_memory_limit" : "1gb",
  "allow_lazy_start" : false,
  "max_num_threads" : 1
}

Regression examples

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The following example creates the house_price_regression_analysis data frame analytics job, the analysis type is regression:

PUT _ml/data_frame/analytics/house_price_regression_analysis
{
  "source": {
    "index": "houses_sold_last_10_yrs"
  },
  "dest": {
    "index": "house_price_predictions"
  },
  "analysis":
    {
      "regression": {
        "dependent_variable": "price"
      }
    }
}

The API returns the following result:

{
  "id" : "house_price_regression_analysis",
  "create_time" : 1656364845151,
  "version" : "8.4.0",
  "authorization" : {
    "roles" : [
      "superuser"
    ]
  },
  "source" : {
    "index" : [
      "houses_sold_last_10_yrs"
    ],
    "query" : {
      "match_all" : { }
    }
  },
  "dest" : {
    "index" : "house_price_predictions",
    "results_field" : "ml"
  },
  "analysis" : {
    "regression" : {
      "dependent_variable" : "price",
      "prediction_field_name" : "price_prediction",
      "training_percent" : 100.0,
      "randomize_seed" : -3578554885299300212,
      "loss_function" : "mse",
      "early_stopping_enabled" : true
    }
  },
  "model_memory_limit" : "1gb",
  "allow_lazy_start" : false,
  "max_num_threads" : 1
}

The following example creates a job and specifies a training percent:

PUT _ml/data_frame/analytics/student_performance_mathematics_0.3
{
 "source": {
   "index": "student_performance_mathematics"
 },
 "dest": {
   "index":"student_performance_mathematics_reg"
 },
 "analysis":
   {
     "regression": {
       "dependent_variable": "G3",
       "training_percent": 70,  
       "randomize_seed": 19673948271  
     }
   }
}

	The percentage of the data set that is used for training the model.
	The seed that is used to randomly pick which data is used for training.

The following example uses custom feature processors to transform the categorical values for DestWeather into numerical values using one-hot, target-mean, and frequency encoding techniques:

PUT _ml/data_frame/analytics/flight_prices
{
  "source": {
    "index": [
      "kibana_sample_data_flights"
    ]
  },
  "dest": {
    "index": "kibana_sample_flight_prices"
  },
  "analysis": {
    "regression": {
      "dependent_variable": "AvgTicketPrice",
      "num_top_feature_importance_values": 2,
      "feature_processors": [
        {
          "frequency_encoding": {
            "field": "DestWeather",
            "feature_name": "DestWeather_frequency",
            "frequency_map": {
              "Rain": 0.14604811155570188,
              "Heavy Fog": 0.14604811155570188,
              "Thunder & Lightning": 0.14604811155570188,
              "Cloudy": 0.14604811155570188,
              "Damaging Wind": 0.14604811155570188,
              "Hail": 0.14604811155570188,
              "Sunny": 0.14604811155570188,
              "Clear": 0.14604811155570188
            }
          }
        },
        {
          "target_mean_encoding": {
            "field": "DestWeather",
            "feature_name": "DestWeather_targetmean",
            "target_map": {
              "Rain": 626.5588814585794,
              "Heavy Fog": 626.5588814585794,
              "Thunder & Lightning": 626.5588814585794,
              "Hail": 626.5588814585794,
              "Damaging Wind": 626.5588814585794,
              "Cloudy": 626.5588814585794,
              "Clear": 626.5588814585794,
              "Sunny": 626.5588814585794
            },
            "default_value": 624.0249512020454
          }
        },
        {
          "one_hot_encoding": {
            "field": "DestWeather",
            "hot_map": {
              "Rain": "DestWeather_Rain",
              "Heavy Fog": "DestWeather_Heavy Fog",
              "Thunder & Lightning": "DestWeather_Thunder & Lightning",
              "Cloudy": "DestWeather_Cloudy",
              "Damaging Wind": "DestWeather_Damaging Wind",
              "Hail": "DestWeather_Hail",
              "Clear": "DestWeather_Clear",
              "Sunny": "DestWeather_Sunny"
            }
          }
        }
      ]
    }
  },
  "analyzed_fields": {
    "includes": [
      "AvgTicketPrice",
      "Cancelled",
      "DestWeather",
      "FlightDelayMin",
      "DistanceMiles"
    ]
  },
  "model_memory_limit": "30mb"
}

These custom feature processors are optional; automatic feature encoding still occurs for all categorical features.

Classification example

edit

The following example creates the loan_classification data frame analytics job, the analysis type is classification:

PUT _ml/data_frame/analytics/loan_classification
{
  "source" : {
    "index": "loan-applicants"
  },
  "dest" : {
    "index": "loan-applicants-classified"
  },
  "analysis" : {
    "classification": {
      "dependent_variable": "label",
      "training_percent": 75,
      "num_top_classes": 2
    }
  }
}