Body

• source object

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

  Hide source attributes Show source attributes object

  • index string | array[string] Required

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

  • runtime_mappings object

    Definitions of runtime fields that will become part of the mapping of the destination index.

    Hide runtime_mappings attribute Show runtime_mappings attribute object

    • * object Additional properties
      Hide * attributes Show * attributes object

      • fields object

        For type composite

        Hide fields attribute Show fields attribute object

        • * object Additional properties
      • fetch_fields array[object]

        For type lookup
      • format string

        A custom format for date type runtime fields.

      • input_field string

        For type lookup

      • target_field string

        For type lookup

      • target_index string

        For type lookup

      • script object

        Painless script executed at query time.

      • type string Required

        Field type, which can be: boolean, composite, date, double, geo_point, ip,keyword, long, or lookup.

        Values are boolean, composite, date, double, geo_point, geo_shape, ip, keyword, long, or lookup.

  • _source 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.

    Hide _source attributes Show _source attributes object

    • includes array[string]

      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.

    • excludes array[string]

      An array of strings that defines the fields that will be included in the analysis.

  • query 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": {}}.

    Query DSL
• dest object

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

  Hide dest attributes Show dest attributes object

  • index string Required

    Defines the destination index to store the results of the data frame analytics job.

  • results_field string

    Defines the name of the field in which to store the results of the analysis. Defaults to ml.

• analysis object

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

  Hide analysis attributes Show analysis attributes object

  • classification object

    The configuration information necessary to perform classification.

    Hide classification attributes Show classification attributes object

    • alpha number

      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 string Required

      Defines which field of the document is to be predicted. It 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. For classification analysis, 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 30 different values in this field. For regression analysis, the data type of the field must be numeric.

    • downsample_factor number

      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. 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 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.

      Default value is true.

    • eta number

      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. By default, this value is calculated during hyperparameter optimization. It must be a value between 0.001 and 1.

    • eta_growth_rate_per_tree number

      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 number

      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 array[object]

      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.
    • gamma number

      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 number

      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 number

      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 number

      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 number

      Advanced configuration option. Specifies the maximum number of feature importance values per document to return. By default, no feature importance calculation occurs.

      Default value is 0.

    • prediction_field_name string

      Defines the name of the prediction field in the results. Defaults to <dependent_variable>_prediction.

    • randomize_seed number

      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 number

      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 number

      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
    • class_assignment_objective string
    • num_top_classes number

      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. NOTE: 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.

      Default value is 2.

  • outlier_detection object

    The configuration information necessary to perform outlier detection. NOTE: 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.

    Hide outlier_detection attributes Show outlier_detection attributes object

    • compute_feature_influence boolean

      Specifies whether the feature influence calculation is enabled.

      Default value is true.

    • feature_influence_threshold number

      The minimum outlier score that a document needs to have in order to calculate its feature influence score. Value range: 0-1.

      Default value is 0.1.

    • method 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.

      Default value is ensemble.

    • n_neighbors number

      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 number

      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 boolean

      If true, the following operation is performed on the columns before computing outlier scores: (x_i - mean(x_i)) / sd(x_i).

      Default value is true.

  • regression object

    The configuration information necessary to perform regression. NOTE: 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.

    Hide regression attributes Show regression attributes object

    • alpha number

      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 string Required

      Defines which field of the document is to be predicted. It 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. For classification analysis, 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 30 different values in this field. For regression analysis, the data type of the field must be numeric.

    • downsample_factor number

      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. 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 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.

      Default value is true.

    • eta number

      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. By default, this value is calculated during hyperparameter optimization. It must be a value between 0.001 and 1.

    • eta_growth_rate_per_tree number

      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 number

      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 array[object]

      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.
    • gamma number

      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 number

      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 number

      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 number

      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 number

      Advanced configuration option. Specifies the maximum number of feature importance values per document to return. By default, no feature importance calculation occurs.

      Default value is 0.

    • prediction_field_name string

      Defines the name of the prediction field in the results. Defaults to <dependent_variable>_prediction.

    • randomize_seed number

      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 number

      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 number

      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
    • loss_function string

      The loss function used during regression. Available options are mse (mean squared error), msle (mean squared logarithmic error), huber (Pseudo-Huber loss).

      Default value is mse.

    • loss_function_parameter number

      A positive number that is used as a parameter to the loss_function.

• description string

  A description of the job.

• model_memory_limit string

  The approximate maximum amount of memory resources that are permitted for analytical processing. If your elasticsearch.yml file contains an xpack.ml.max_model_memory_limit setting, an error occurs when you try to create data frame analytics jobs that have model_memory_limit values greater than that setting.

  Default value is 1gb.

• max_num_threads number

  The maximum number of threads to be used by the analysis. 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.

  Default value is 1.

• analyzed_fields 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.

  Hide analyzed_fields attributes Show analyzed_fields attributes object

  • includes array[string]

    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.

  • excludes array[string]

    An array of strings that defines the fields that will be included in the analysis.

• allow_lazy_start boolean

  Specifies whether this job can start when there is insufficient machine learning node capacity for it to be immediately assigned to a node.

  Default value is false.