honeymoose
/
OpenSearch
mirror of https://github.com/honeymoose/OpenSearch.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

[DOCS] Adds ml-cpp PRs to release notes (#49185)

This commit is contained in:
Lisa Cawley 2019-11-15 09:36:39 -08:00 committed by GitHub
parent dad68c59fe
commit de8107e350
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 21 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
docs/reference/release-notes/7.5.asciidoc
View File

@ -154,7 +154,24 @@ Machine Learning::
* Add lazy assignment job config option {pull}47726[#47726]
* Additional outlier detection parameters {pull}47600[#47600]
* More accurate job memory overhead {pull}47516[#47516]
* Throttle the delete-by-query of expired results {pull}47177[#47177] (issues: {issue}47003[#47003], {issue}47103[#47103])
* Throttle the delete-by-query of expired results {pull}47177[#47177] (issue: {issue}47003[#47003])
* Improve performance and concurrency training boosted tree regression models.
For large data sets, this change was observed to give a 10% to 20% decrease in
train time. {ml-pull}622[#622]
* Upgrade Boost libraries to version 1.71 {ml-pull}638[#638]
* Improve initialisation of boosted tree training. This generally enables us to
find lower loss models faster. {ml-pull}686[#686]
* Include a smooth tree depth based penalty to regularized objective function for
boosted tree training. Hard depth based regularization is often the strategy of
choice to prevent over fitting for XGBoost. By smoothing, we can make better tradeoffs.
Also, the parameters of the penalty function are more suited to optimising with our
Bayesian optimisation based hyperparameter search. {ml-pull}698[#698]
* Binomial logistic regression targeting cross entropy {ml-pull}713[#713]
* Improvements to count and sum anomaly detection for sparse data. This primarily
aims to improve handling of data which are predictably present: detecting when they
are unexpectedly missing. {ml-pull}721[#721]
* Trap numeric errors causing bad hyperparameter search initialisation and repeated
errors to be logged during boosted tree training {ml-pull}732[#732]
Mapping::
* Add migration tool checks for _field_names disabling {pull}46972[#46972] (issues: {issue}42854[#42854], {issue}46681[#46681])
@ -317,12 +334,14 @@ MULTIPLE AREA LABELS::
* Fix cluster alert for watcher/monitoring IndexOutOfBoundsExcep… {pull}45308[#45308] (issue: {issue}43184[#43184])
Machine Learning::
* Deduplicate multi-fields for data frame analytics {pull}48799[#48799] (issues: {issue}48756[#48756], {issue}48770[#48770])
* Deduplicate multi-fields for data frame analytics {pull}48799[#48799] (issue: {issue}48756[#48756])
* Prevent fetching multi-field from source {pull}48770[#48770] (issue: {issue}48756[#48756])
* Fix detection of syslog-like timestamp in find_file_structure {pull}47970[#47970]
* Fix serialization of evaluation response. {pull}47557[#47557]
* Reinstate ML daily maintenance actions {pull}47103[#47103] (issue: {issue}47003[#47003])
* Fix two datafeed flush lockup bugs {pull}46982[#46982]
* Restore from checkpoint could damage seasonality modeling. For example, it could
cause seasonal components to be overwritten in error. {ml-pull}821[#821]
NOT CLASSIFIED::
* Remove uniqueness constraint for API key name and make it optional {pull}47549[#47549] (issue: {issue}46646[#46646])
@ -383,7 +402,6 @@ Task Management::
Transform::
* Do not fail checkpoint creation due to global checkpoint mismatch {pull}48423[#48423] (issue: {issue}48379[#48379])
* Prevent assignment if any node is older than 7.4 {pull}48055[#48055] (issue: {issue}48019[#48019])
* Prevent assignment to nodes older than 7.4 {pull}48044[#48044] (issue: {issue}48019[#48019])
* Fix bwc serialization with 7.3 {pull}48021[#48021]
* Signal listener early on task _stop failure {pull}47954[#47954]
* Use field_caps API for mapping deduction {pull}46703[#46703] (issue: {issue}46694[#46694])