Adds a new parameter for classification that enables choosing whether to assign labels to
maximise accuracy or to maximise the minimum class recall.
Fixes#52427.
The version replacement for the code snippet should replace 7.6 with the current version,
but doesn't match because of a missing whitespace.
Closes#51052
Adds a new parameter to regression and classification that enables computation
of importance for the top most important features. The computation of the importance
is based on SHAP (SHapley Additive exPlanations) method.
Backport of #50914
This adds a new `randomize_seed` for regression and classification.
When not explicitly set, the seed is randomly generated. One can
reuse the seed in a similar job in order to ensure the same docs
are picked for training.
Backport of #49990
This adds a `_source` setting under the `source` setting of a data
frame analytics config. The new `_source` is reusing the structure
of a `FetchSourceContext` like `analyzed_fields` does. Specifying
includes and excludes for source allows selecting which fields
will get reindexed and will be available in the destination index.
Closes#49531
Backport of #49690
This change adds:
- A new option, allow_lazy_open, to anomaly detection jobs
- A new option, allow_lazy_start, to data frame analytics jobs
Both work in the same way: they allow a job to be
opened/started even if no ML node exists that can
accommodate the job immediately. In this situation
the job waits in the opening/starting state until ML
node capacity is available. (The starting state for data
frame analytics jobs is new in this change.)
Additionally, the ML nightly maintenance tasks now
creates audit warnings for ML jobs that are unassigned.
This means that jobs that cannot be assigned to an ML
node for a very long time will show a yellow warning
triangle in the UI.
A final change is that it is now possible to close a job
that is not assigned to a node without using force.
This is because previously jobs that were open but
not assigned to a node were an aberration, whereas
after this change they'll be relatively common.
Adds the following parameters to `outlier_detection`:
- `compute_feature_influence` (boolean): whether to compute or not
feature influence scores
- `outlier_fraction` (double): the proportion of the data set assumed
to be outlying prior to running outlier detection
- `standardization_enabled` (boolean): whether to apply standardization
to the feature values
Backport of #47600
* [DOCS] Adds examples to the PUT dfa and the evaluate dfa APIs.
* [DOCS] Removes extra lines from examples.
* Update docs/reference/ml/df-analytics/apis/evaluate-dfanalytics.asciidoc
Co-Authored-By: Lisa Cawley <lcawley@elastic.co>
* Update docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc
Co-Authored-By: Lisa Cawley <lcawley@elastic.co>
* [DOCS] Explains examples.
* [DOCS] Adds regression analytics resources and examples to the data frame analytics APIs.
Co-Authored-By: Benjamin Trent <ben.w.trent@gmail.com>
Co-Authored-By: Tom Veasey <tveasey@users.noreply.github.com>
This PR addresses the feedback in https://github.com/elastic/ml-team/issues/175#issuecomment-512215731.
* Adds an example to `analyzed_fields`
* Includes `source` and `dest` objects inline in the resource page
* Lists `model_memory_limit` in the PUT API page
* Amends the `analysis` section in the resource page
* Removes Properties headings in subsections
Lisa Cawley
Tom Veasey
Dimitris Athanasiou
Christoph Büscher
István Zoltán Szabó
David Roberts
James Rodewig