v0.11.5

MLJBase v0.11.5

Diff since v0.11.4

• rebinding of measure names to non-abbreviated versions of their aliases

• (enhancement) Allow specification of shuffle=... and rng=... in unpack (#167, PR #190)

• (enhancement) Enable loading of datasets from OpenML with OpenML.load

Closed issues:

• Remove two letter measures (#187)

Merged pull requests:

• OpenML API (#182) (@darenasc)
• export forgotten fp, fn, tp, tn - for a 0.11.4 release (#186) (@ablaom)
• Bind measure names to non-abbreviated forms. Abandon tp, tn, fp, fn. (#189) (@ablaom)
• Allow shuffling in unpack (#190) (@ablaom)
• For a 0.11.5 release (#191) (@ablaom)

v0.11.4

MLJBase v0.11.4

Diff since v0.11.3

Add forgotten exports: tp, fp, tn, fn

Merged pull requests:

• fix false/true mix-up in finite measure doc strings (#185) (@ablaom)

v0.11.3

MLJBase v0.11.3

Diff since v0.11.2

Add docstrings to remaining measures

Closed issues:

• Add pkg= in docstring_ext (#57)
• tweaks to BrierScore traits (#172)

Merged pull requests:

• Dev (#180) (@tlienart)
• Add remaining doc strings for measures; For a 0.11.3 release (#184) (@ablaom)

v0.11.2

MLJBase v0.11.2

Diff since v0.11.1

Closed issues:

• Towards next release (#145)

Merged pull requests:

• Install TagBot as a GitHub Action (#174) (@JuliaTagBot)
• Crayon (#179) (@tlienart)

v0.11.1

v0.11.1 (2020-02-11)

Diff since v0.11.0

Merged pull requests:

• Dev (#176) (tlienart)

v0.11.0

v0.11.0 (2020-02-07)

Diff since v0.10.1

Closed issues:

• confusion matrix: Problem with perm=... (#170)
• Possibly add fit\_predict (#161)
• Additional traits for measures (#98)
• Rename default_measure to default_loss and make it a function of the scitype only (#51)

Merged pull requests:

• Minor release with MLJModelInterface and MLJScientificTypes (#173) (tlienart)

v0.10.1

v0.10.1 (2020-01-15)

Diff since v0.10.0

Closed issues:

• export coerce! (#149)
• [tests] Deprecation warning Supervised/Unsupervised task (#144)

Merged pull requests:

• For 0.10.1 release (#162) (ablaom)

v0.10.0

• Give the partition function a new keyword argument stratify=nothing for specifying a Finite vector on which to base stratified partitioning. Query ?partition for details (#113)

• Optimize selectrows for DataFrames, row tables, and column tables (JuliaAI/MLJ.jl#122)

• Add new methods for generating synthetic data sets: make_blobs, make_moons, make_circles, make_regression (#155)

• Update to ScientifcTypes 0.5.1

• Import resampling interface and its implementations from MLJ

• Improve show method for the results of performance evaluations (calling evaluate!, evaluate)

• Add keyword argument repeats=1 to evaluate!/evaluate for repeated resampling. For example, specifying resampling=CV(nfolds=3, shuffle=true), repeats=2 is to generate 6 per_fold performance estimates for aggregation. Query ?evaluate! for details (JuliaAI/MLJ.jl#406)

• Import one-dimensional ranges (ParamRange objects) MLJ and add enhancements: unbounded nominal ranges allowed, with new origin and scale fields; grid generation for unbounded ranges. Allow specification of a type rather than a model in the constructor. Query ?range and ?iterator for details.

• Add a model trait called hyperparameter_ranges to allow model implementers to specify default one-dimensional ranges for hyper parameters

• (breaking) Default for is_julia(::Type{<:Model) is returned to false instead of missing to prevent case-distinction headaches downstream

• improve show method for MLJType objects that "show as constructed" (JuliaAI/MLJ.jl#351)

v0.9.2

• add functionalities to generate foo data (PR #136 and #155, issue #118)
• export coerce! (PR #155, issue #149)
• stratified sampling for partition (PR #138, issue #113)
• minor improvements for cross entropy and briers score (PR #148)
• improve performance of selectrows for DataFrames and julia native tables (PR #154)

v0.9.1

Fix bug in @from_network (also effecting @pipeline) preventing use from MLJ without first doing import MLJBase.