[Diff since v0.20.6](v0.20.6...v0.20.7)

**Merged pull requests:**
- Make subpages collapse in manual sidebar. No new release (docs only) (#1131) (@ablaom)
- Regenerate documentation. (#1132) (@ablaom)
- Update FeatureSelection compat (#1136) (@ablaom)
- For a 0.20.7 release (#1138) (@ablaom)

**Closed issues:**
- Recursive Feature Elimination RFE - Feature Request? (#426)
- For 0.17 release (#864)
- Transformers that need to see target (eg, recursive feature elimination) (#874)
- MLJ API for Missing Imputation ? (#950)
- [Tracking issue] Add `raw_training_scores`accessor function (#960)
- Extract probability for a tresholded model (#981)
- Load data that support the Tables.jl interface (#988)
- Add new sk-learn models to the docs (#1066)
- Improve documentation by additional hierarchy (#1094)
-  Link in examples on CV Recursive Feature Elimination into the manual or in the planned tutorial interface.  (#1129)
- broken link for UnivariateFinite doc string (#1130)
- Add pipeline support for `Unsupervised` models that have a target in `fit` (#1134)
- InteractionTransformer is missing from the "Transformers and Other..." manual page (#1135)

[Diff since v0.20.5](v0.20.5...v0.20.6)

- (**new functionality**) Add `RecursiveFeatureElimination` model wrapper.

**Merged pull requests:**
- CompatHelper: bump compat for MLJFlow to 0.5 (#1122) (@github-actions[bot])
- Add model wrappers to the Model Browser (#1127) (@ablaom)
- For a 0.20.6 release (#1128) (@ablaom)

**Closed issues:**
- Requesting better exposure to MLJFlux in the model browser (#1110)
- Remove `info(rms)` from the cheatsheet (#1117)
- Enable entry of model wrappers into the MLJ Model Registry (#1125)

[Diff since v0.20.4](v0.20.4...v0.20.5)

**Merged pull requests:**
- Tweak the cheatsheet again (#1121) (@ablaom)
- For a 0.20.5 release (#1123) (@ablaom)

[Diff since v0.20.3](v0.20.3...v0.20.4)

- Bump the requirement for MLFlow to 0.4.2. This is technically breaking (but not marked as such because MLJFlow integration is considered expermental). With latest version of MLFlowClient installed, where previously you would define `logger=MLJFlow.Logger("http:https://127.0.0.1:5000/")` you must now do `logger=MLJFlow.Logger("http:https://127.0.0.1:5000/api")` or similar; see also https://github.com/JuliaAI/MLFlowClient.jl/releases/tag/v0.5.1.

**Merged pull requests:**
- Add PartionedLS.jl model to docs and browser (#1103) (@ablaom)
- Update documentation. No new release. (#1104) (@ablaom)
- Update ROADMAP.md (#1106) (@ablaom)
- Use repl language tag for sample (#1107) (@abhro)
- Update cheatsheet and workflow docs (#1109) (@ablaom)
- Force documentation updates. No new release. (#1112) (@ablaom)
- Updates now that MLJ.jl has been moved to the JuliaAI GitHub organization (#1113) (@DilumAluthge)
- Remove Telco example (#1114) (@ablaom)
- Suppress model-generated warnings in integration tests (#1115) (@ablaom)
- Upgrading MLJFlow.jl to v0.4.2 (#1118) (@pebeto)
- For a 0.20.4 release (#1120) (@ablaom)

**Closed issues:**
- Curated list of models (#716)
- Migrate MLJ from alan-turing-institute to JuliaAI? (#829)
- Update the binder demo for MLJ (#851)
- Add wrappers for clustering to get uniform interface (#982)
- Confusing Julia code in adding_models_for_general_use.md (#1061)
- feature_importances for Pipeline including XGBoost don't work (#1100)
- Current performance evaluation objects, recently added to TunedModel histories, are too big (#1105)
- Update cheat sheet instance of depracated `@from_network` code (#1108)

[Diff since v0.20.2](v0.20.2...v0.20.3)

- Bump compat for MLJFlow to 0.4 to buy into `MLJBase.save` method ambiguity fix (in MLJFlow 0.4.1).

**Merged pull requests:**
- Clarify `input_scitype` for Static models (#1076) (@ablaom)
- Documentation updates (#1077) (@ablaom)
- Add integration tests (#1079) (@ablaom)
- Test new integration tests. No new release.  (#1080) (@ablaom)
- Fix the integration tests (#1081) (@DilumAluthge)
- Move EvoLinear into [extras] where it belongs (#1083) (@ablaom)
- CI: split the integration tests into a separate job (#1086) (@DilumAluthge)
- CI tweaks (#1087) (@ablaom)
- Update list_of_supported_models for betaml (#1089) (@sylvaticus)
- Update ModelDescriptors.toml for BetaML models (#1090) (@sylvaticus)
- Update documentation to reflect recent BetaML reorganisation (#1091) (@ablaom)
- Replace relevant sections of manual with links to the new MLJModelInterface docs. (#1095) (@ablaom)
- Update docs. No new release (#1096) (@ablaom)
- Update getting_started.md to avoid error from line 338 (#1098) (@caesquerre)
- For a 0.20.3 release (#1102) (@ablaom)

**Closed issues:**
- Meta issue: lssues for possible collaboration with UCL (#673)
- Integration test failures: Classifiers (#939)
- Oversample undersample (#983)
- Add AutoEncoderMLJ model (part of BetaML) (#1074)
- Add new model descriptors to fix doc-generation fail (#1084)
- Update list of BetaML models (#1088)
- Upate ROADMAP.md (#1093)
- Deserialisation fails for wrappers like `TunedModel` when atomic model overloads `save/restore` (#1099)

[Diff since v0.20.1](v0.20.1...v0.20.2)

- Replace `MLFlowLogger` with `MLJFlow.Logger`; see [here](https://github.com/JuliaAI/MLJFlow.jl/releases/tag/v0.3.0). So a logger instance is now instantiated with `using MLJFlow; logger = MLJFlow.Logger(baseuri)`. This is technically breaking but not tagged as such, because MLFlow integration is still [experimental](https://alan-turing-institute.github.io/MLJ.jl/dev/logging_workflows/#Logging-Workflows).

**Merged pull requests:**
- Fix MLJTuning.jl links (#1068) (@jd-foster)
- CompatHelper: add new compat entry for Statistics at version 1, (keep existing compat) (#1070) (@github-actions[bot])
- Bump compat: MLJFlow 0.3  (#1072) (@ablaom)
- For a 0.20.2 release (#1073) (@ablaom)

**Closed issues:**
- Export the name `MLJFlow` (#1067)
- `evaluate` errors  (#1069)

[Diff since v0.20.0](v0.20.0...v0.20.1)

- (**new feature**) Add the `BalancedModel` wrapper from MLJBalancing.jl (#1064)
- (**docs**) Add the over/undersampling models from Imbalance.jl to the Model Browser  (#1064)

**Merged pull requests:**
- Add MLJBalancing to MLJ and add class imbalance docs (#1064) (@ablaom)
- For a 0.20.1 release (#1065) (@ablaom)

**Closed issues:**
- Oversampling and undersampling (#661)
- [Tracking] Migration of measures MLJBase.jl -> StatisticalMeasures.jl (#1053)
- Include MLJBalancing.jl in MLJ and re-export it's names.  (#1062)
- Update docs for new class imbalance support (#1063)

[Diff since v0.19.5](v0.19.5...v0.20.0)

-  (**breaking**) Adapt to the migration of measures from MLJBase.jl to StatisticalMeasures.jl (#1054). See the [MLJBase 1.0 migration guide](https://github.com/alan-turing-institute/MLJ.jl/blob/dev/docs/src/performance_measures.md#migration-guide-for-changes-to-measures-in-mljbase-10) for details.

**Merged pull requests:**
- CI: fix the YAML syntax for the docs job, and thus properly surface any docbuild failures (#1046) (@DilumAluthge)
- Update docs (#1048) (@ablaom)
- Try again to generate the documentation (#1049) (@ablaom)
- `docs/make.jl`: set `devbranch` to `master`, which means that the docs will be deployed for pushes to `master (#1051) (@DilumAluthge)
- Try to deploy docs again x 3 (#1052) (@ablaom)
- Adapt to migration of measures MLJBase.jl -> StatisticalMeasures.jl (#1054) (@ablaom)
- For a 0.20 release (#1060) (@ablaom)

**Closed issues:**
- Julia crashes when fitting a SVC (#1030)
- Update deprecated document example in "Transformers ..." section of manual (#1040)
- `fit!` not exported in 0.19.3/0.19.4? (#1041)
- Doc generation is failing silently (#1045)

[Diff since v0.19.4](v0.19.4...v0.19.5)

- Correct problem with previous version in which some methods were not exported, namely: `source`, `node`, `fit!`, `freeze!`, `thaw!`, `Node`, `sources`, `origins` (#1043) @pebeto

**Closed issues:**
- Is the Averager documentation deprecated? (#1039)

**Merged pull requests:**
- Adding necessary exports (#1043) (@pebeto)
- For a 0.19.5 release (#1044) (@ablaom)

[Diff since v0.19.3](v0.19.3...v0.19.4)

**Merged pull requests:**
- Updating MLJBase.jl dep to last version (#1037) (@pebeto)