Tags: JuliaAI/MLJBase.jl
Tags
[Diff since v1.5.0](v1.5.0...v1.6.0) - (**enhancment**) Arrange that pipelines support transformers that need a target variable for training (#984) **Merged pull requests:** - Docstring patches (#983) (@abhro) - Add support in pipelines for `Unsupervised` models for which `target_in_fit` is `true` (#984) (@ablaom) - Fix `default_logger` docstring (#986) (@ablaom) - For a 1.6 release (#987) (@ablaom)
[Diff since v1.2.1](v1.2.1...v1.3.0) - (**Performance enhancement**) Remove type instability for `predict(mach::Machine, ...)` in the easy and typical case that `mach` does not wrap a `Symbol` model (#969) - (**New feature**) Give `evaluate` and `evaluate!` the option `compact=true`, to return a `CompactPerformanceEvaluation` object with minimal memory footprint (#973) - (**New feature**) Add an `InSample()` resampling strategy that trains and tests on the same data (whatever is specified by `rows`, or all supplied data) (#975) - (**Display improvement**) Split the table displayed as part of an `PerformanceEvaluation` object over two tables, if needed, to deal with overly wide tables (#973) **Merged pull requests:** - Add prompt to docstring REPL example (#968) (@abhro) - Address some predict/transform type instabilities (#969) (@ablaom) - Update docstring examples and code (#970) (@abhro) - Make test of `iterator(...)` more robust (#972) (@ablaom) - Add `CompactPerformanceEvaluation` objects and the option in `evaluate!` to construct them (#973) (@ablaom) - Add `InSample` resampling strategy (#975) (@ablaom) - For a 1.3 release (#977) (@ablaom) **Closed issues:** - Add `InSample` resampling strategy (#967) - Tests failing on dev (#971) - `selectrows` struggling with views of a table (#974)
[Diff since v1.1.2](v1.1.2...v1.2.0) - (**enhancement**) Expose `feature_importances` in pipelines with a supporting supervised component, and in `TransformedTargetModel`s with supporting atomic model (#963) **Merged pull requests:** - Fix some error message diagnostics (#962) (@ablaom) - Allow `Pipeline` and `TransformedTargetModel` to support feature importances (#963) (@ablaom) - For a 1.2.0 release (#964) (@ablaom)
[Diff since v1.1.1](v1.1.1...v1.1.2) **Merged pull requests:** - Reduce sigdigits in parameter range display #948 (#958) (@adarshpalaskar1) - Fix problem with serialization of nested models when component model overload `save`/`restore` (#960) (@ablaom) - For a 1.1.2 release (#961) (@ablaom) **Closed issues:** - Serialized Composite Model Fails with XGBoost (#927) - Reduce sigdigits in parameter range display (#948)
[Diff since v1.1.0](v1.1.0...v1.1.1) **Merged pull requests:** - Fix Resampler update bug (#954) (@ablaom) - For a 1.1.1 release (#955) (@ablaom) - Bump 1.1.1 (#956) (@ablaom) - For a 1.1.1 release - take II (#957) (@ablaom) **Closed issues:** - Update the class imbalance POC (#887) - Post issues pointing to MLJBase 1.0 migration guide (#937) - strategy or example for doing a stratified k-fold (#950)
[Diff since v1.0.1](v1.0.1...v1.1.0) - Arrange that calling `plot(mach)` on a machine `mach` calls `plot(mach.fitresult)`, allowing model implementations to define plot recipes locally and have them work on machines wrapping their models (#951) **Merged pull requests:** - Remove measures from docs (#945) (@ablaom) - CompatHelper: add new compat entry for Statistics at version 1, (keep existing compat) (#949) (@github-actions[bot]) - Forward `(::Machine).fitresult` to RecipesBase (#951) (@MilesCranmer) - bump 1.1.0 (#952) (@ablaom) - For a 1.1.0 release (#953) (@ablaom) **Closed issues:** - Measures page in documentation is empty (#944)
PreviousNext