elasticsearch-js/docs/reference/api/MlDataframeAnalysis.md

MlDataframeAnalysis [interface-MlDataframeAnalysis]

Name	Type	Description
alpha	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	string	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	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. 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.
eta_growth_rate_per_tree	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.
eta	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. By default, this value is calculated during hyperparameter optimization. It must be a value between 0.001 and 1.
feature_bag_fraction	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	MlDataframeAnalysisFeatureProcessor[]	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	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	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	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	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.
maximum_number_trees	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. max_trees
num_top_feature_importance_values	integer	Advanced configuration option. Specifies the maximum number of feature importance values per document to return. By default, no feature importance calculation occurs.
prediction_field_name	Field	Defines the name of the prediction field in the results. Defaults to <dependent_variable>_prediction.
randomize_seed	double	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	integer	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	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	Percentage	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.