[role="xpack"] [testenv="platinum"] [[ml-dfa-analysis-objects]] === Analysis configuration objects {dfanalytics-cap} resources contain `analysis` objects. For example, when you create a {dfanalytics-job}, you must define the type of analysis it performs. This page lists all the available parameters that you can use in the `analysis` object grouped by {dfanalytics} types. [discrete] [[oldetection-resources]] ==== {oldetection-cap} configuration objects An `outlier_detection` configuration object has the following properties: `compute_feature_influence`:: (Optional, boolean) include::{docdir}/ml/ml-shared.asciidoc[tag=compute-feature-influence] `feature_influence_threshold`:: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=feature-influence-threshold] `method`:: (Optional, string) include::{docdir}/ml/ml-shared.asciidoc[tag=method] `n_neighbors`:: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=n-neighbors] `outlier_fraction`:: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=outlier-fraction] `standardization_enabled`:: (Optional, boolean) include::{docdir}/ml/ml-shared.asciidoc[tag=standardization-enabled] [discrete] [[regression-resources]] ==== {regression-cap} configuration objects [source,console] -------------------------------------------------- PUT _ml/data_frame/analytics/house_price_regression_analysis { "source": { "index": "houses_sold_last_10_yrs" <1> }, "dest": { "index": "house_price_predictions" <2> }, "analysis": { "regression": { <3> "dependent_variable": "price" <4> } } } -------------------------------------------------- // TEST[skip:TBD] <1> Training data is taken from source index `houses_sold_last_10_yrs`. <2> Analysis results will be output to destination index `house_price_predictions`. <3> The regression analysis configuration object. <4> Regression analysis will use field `price` to train on. As no other parameters have been specified it will train on 100% of eligible data, store its prediction in destination index field `price_prediction` and use in-built hyperparameter optimization to give minimum validation errors. [float] [[regression-resources-standard]] ===== Standard parameters `dependent_variable`:: (Required, string) include::{docdir}/ml/ml-shared.asciidoc[tag=dependent-variable] + -- The data type of the field must be numeric. -- `prediction_field_name`:: (Optional, string) include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name] `training_percent`:: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=training-percent] `randomize_seed`:: (Optional, long) include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed] [float] [[regression-resources-advanced]] ===== Advanced parameters Advanced parameters are for fine-tuning {reganalysis}. They are set automatically by <> to give minimum validation error. It is highly recommended to use the default values unless you fully understand the function of these parameters. If these parameters are not supplied, their values are automatically tuned to give minimum validation error. `eta`:: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=eta] `feature_bag_fraction`:: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=feature-bag-fraction] `maximum_number_trees`:: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=maximum-number-trees] `gamma`:: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=gamma] `lambda`:: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=lambda] [discrete] [[classification-resources]] ==== {classification-cap} configuration objects [float] [[classification-resources-standard]] ===== Standard parameters `dependent_variable`:: (Required, string) include::{docdir}/ml/ml-shared.asciidoc[tag=dependent-variable] + -- The data type of the field must be numeric or boolean. -- `num_top_classes`:: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=num-top-classes] `prediction_field_name`:: (Optional, string) include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name] `training_percent`:: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=training-percent] `randomize_seed`:: (Optional, long) include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed] [float] [[classification-resources-advanced]] ===== Advanced parameters Advanced parameters are for fine-tuning {classanalysis}. They are set automatically by <> to give minimum validation error. It is highly recommended to use the default values unless you fully understand the function of these parameters. If these parameters are not supplied, their values are automatically tuned to give minimum validation error. `eta`:: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=eta] `feature_bag_fraction`:: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=feature-bag-fraction] `maximum_number_trees`:: (Optional, integer) include::{docdir}/ml/ml-shared.asciidoc[tag=maximum-number-trees] `gamma`:: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=gamma] `lambda`:: (Optional, double) include::{docdir}/ml/ml-shared.asciidoc[tag=lambda] [discrete] [[ml-hyperparam-optimization]] ==== Hyperparameter optimization If you don't supply {regression} or {classification} parameters, hyperparameter optimization will be performed by default to set a value for the undefined parameters. The starting point is calculated for data dependent parameters by examining the loss on the training data. Subject to the size constraint, this operation provides an upper bound on the improvement in validation loss. A fixed number of rounds is used for optimization which depends on the number of parameters being optimized. The optimization starts with random search, then Bayesian optimization is performed that is targeting maximum expected improvement. If you override any parameters, then the optimization will calculate the value of the remaining parameters accordingly and use the value you provided for the overridden parameter. The number of rounds are reduced respectively. The validation error is estimated in each round by using 4-fold cross validation.