v0.19.3

MLJ v0.19.3

Diff since v0.19.2

Closed issues:

• SymbolicRegression.jl — registry update (#1032)

Merged pull requests:

• feat: Update ROADMAP.md be more understandable (#1031) (@MelihDarcanxyz)
• add sirus.jl and symbolicregression.jl models to model browser (#1033) (@OkonSamuel)
• Add MLJFlow for integration with MLflow logging platform (#1034) (@ablaom)

v0.19.2

MLJ v0.19.2

Diff since v0.19.1

Closed issues:

• @from_network does more strange eval stuff (#703)
• Create new package for MLJ-universe-wide integration tests (#885)
• Stack of TunedModels (#980)
• Please add CatBoost or any alternate package (pure Julia) which can beat it (#992)
• Update list of models for BetaML (#993)
• Update List of Supported Models Clustering.jl Section (#1000)
• predict should work on DataFrameRow (#1004)
• Documentation generation fails silently (#1007)
• Clarify and fix documentation around reformat. (#1010)
• Reporting a vulnerability (#1015)
• What causes the Distributed.ProcessExitedException(3) error in Julia and how can I resolve it in my Pluto notebook? (#1018)
• Add link to Mt Everest blog (#1021)
• Remove "experimental" label for acceleration API docs (#1026)

Merged pull requests:

• Fix TransformedTarget example in manual (no new release) (#999) (@ablaom)
• updating Clustering.jl model list to address #1000 (#1001) (@john-waczak)
• Add CatBoost to list of models and 3rd party packages (#1002) (@ablaom)
• Some small documentations improvements. Not to trigger a new release. (#1003) (@ablaom)
• Add auto-generated Model Browser section to the manual (#1005) (@ablaom)
• Add new auto-generated Model Browser section to the manual. Not to trigger new release. (#1006) (@ablaom)
• Add Model Browser entry for SelfOrganizingMap (#1008) (@ablaom)
• Update documentation (#1009) (@ablaom)
• Clarify data front-end in docs (#1011) (@ablaom)
• Doc fixes. No new release. (#1012) (@ablaom)
• Update model browser and list of models to reflect addition of CatBoost.jl and some OutlierDetectionPython.jl models (#1013) (@ablaom)
• Update to the manual. No new release. (#1014) (@ablaom)
• Make docs fail on error (#1017) (@rikhuijzer)
• Cleaned up Adding Models for General Use documentation (#1019) (@antoninkriz)
• CompatHelper: bump compat for StatsBase to 0.34, (keep existing compat) (#1020) (@github-actions[bot])
• Remove CatBoost.jl from third party packages (#1024) (@tylerjthomas9)

v0.19.1

MLJ v0.19.1

Diff since v0.19.0

Closed issues:

• Support for ProbabilisticSet type in MLJModelInterface.jl (#978)
• question about Isotonic Regression (#986)
• predict_mode of pipeline model return a UnivariateFinite after upgrade to 0.19.0 (#987)
• MLJ Tuning optimizers are no working with julia 1.8.3 and julia 1.9.0 (#990)
• WARNING: both MLJBase and DataFrames export "transform"; uses of it in module Main must be qualified (#991)
• CURANDError: kernel launch failure (code 201, CURAND_STATUS_LAUNCH_FAILURE) (#997)

Merged pull requests:

• Document changes and sundries. No new release. (#985) (@ablaom)
• (re) updated model names of BetaML (#994) (@sylvaticus)
• Exclude bib, md, and drawio from repo stats (#995) (@rikhuijzer)
• For a 0.19.1 release (#998) (@ablaom)

v0.19.0

MLJ v0.19.0

Diff since v0.18.6

MLJBase compatibility is bumped to 0.21 and MLJModels compatibility is bumped to 0.16. This makes a new simplified method for exporting learning networks available but also introduces some breaking changes:

• (mildy breaking) The value method is no longer exported by MLJ as essentially private (#891)
• MLJBase 0.21 release notes
• MLJModels 0.16 release notes

Closed issues:

• Do not re-export value (#891)
• Large models name change in BetaML (#963)
• Add ConformalPrediction.jl to list of 3rd party packages (#967)
• Documentation for BinaryThresholdPredictor (#973)

Merged pull requests:

• Update docs for new learning network export method (#972) (@ablaom)
• added to docs (#979) (@pat-alt)
• For a 0.19 release (#984) (@ablaom)

v0.18.6

MLJ v0.18.6

Diff since v0.18.5

Closed issues:

• DBSCAN from Clustering.jl not registered (#845)
• Update manual re new reporting_operations trait (#956)
• Improvement in the Preparing Data part (#964)
• serializable and restore! should be "safe" to use any time (#965)
• Adds EvoLinearRegressor to list of models (#966)
• export InteractionTransformer from MLJModels (#969)
• Encoders for feature engineering (#970)
• Clarify meaning of "table" in documentation (#971)
• re-export serializable and restore! (#975)

Merged pull requests:

• Updated names of BetaML models (#968) (@sylvaticus)
• add the interface package to add (#974) (@xgdgsc)
• Minor doc improvements and new exports (#976) (@ablaom)
• For a 0.18.6 release (#977) (@ablaom)

v0.18.5

MLJ v0.18.5

Diff since v0.18.4

Merged pull requests:

• Fix mini typos in docs (#958) (@svilupp)
• Documentation updates (#959) (@ablaom)
• Bump version (#961) (@jbrea)
• For a 0.18.5 release (#962) (@ablaom)

v0.18.4

MLJ v0.18.4

Diff since v0.18.3

Closed issues:

• Tutorial not working (#951)
• Bump MLJBase compat and re-export feature_importances (#954)

Merged pull requests:

• Update link for telco example to Data Science Tutorials version (#952) (@ablaom)
• Update API spec re training losses and feature importances (#953) (@ablaom)
• For a 0.18.4 release (#955) (@ablaom)

v0.18.3

MLJ v0.18.3

Diff since v0.18.2

Closed issues:

• Feature request: ability to convert scitype warnings into errors (#908)
• Confusing true_negative(x, y) error (#919)
• Show is too long for MulticlassPrecision and MulticlassTruePositiveRate (#923)
• DOC: Link giving 404 not found (#929)
• Re-export scitype_check_level (#936)
• models(matching(X, y)) returns empty but shouldn't (#937)
• LoadError on Getting Started Fit and Predict exercise (#940)
• Change in Julia version generating the Manifest.toml 's ? (#941)
• export PerformanceEvaluation (#944)
• Make docs regarding Random Forest and Ensebles more clear (#945)
• Compile time for DataFrames, typename hack not working (#946)
• Factor out performance evaluation tools (#947)

Merged pull requests:

• Fix level of confidence interval in telco example to make it 95% (#938) (@ablaom)
• Update the list of models in the manual (#942) (@ablaom)
• Re-export default_scitype_check_level (#943) (@ablaom)
• Clarify homogeneous ensembles comments (#948) (@ablaom)
• For a 0.18.3 release (#949) (@ablaom)

v0.18.2

MLJ v0.18.2

Diff since v0.18.1

Closed issues:

• Update Save method documentation (#899)
• DOC: Link giving 404 not found (#929)
• Question about using acceleration to implement parallelism (#934)

Merged pull requests:

• Fix a table in telco tutorial (#927) (@ablaom)
• add MLCourse (#928) (@jbrea)
• Add link to MLCourse in the documentation (#930) (@ablaom)
• Move EPFL course up the list on "Learning MLJ" page (#931) (@ablaom)
• Add OneRuleClassifier to list of models in manual (#932) (@ablaom)
• For a 0.18.2 release (#935) (@ablaom)

v0.18.1

MLJ v0.18.1

Diff since v0.18.0

• Re-export doc from MLJModels and bump compat of same

Merged pull requests:

• For a 0.18.1 release (#926) (@ablaom)