v0.4.0

• (Enhancment) Update to MLJBase 0.5.0 and MLJModels 0.4.0. The
  following new scikit-learn models are thereby made available:

• ScikitLearn.jl
• SVM: SVMClassifier, SVMRegressor, SVMNuClassifier,
  SVMNuRegressor, SVMLClassifier, SVMLRegressor,
• Linear Models (regressors): ARDRegressor,
  BayesianRidgeRegressor, ElasticNetRegressor,
  ElasticNetCVRegressor, HuberRegressor, LarsRegressor,
  LarsCVRegressor, LassoRegressor, LassoCVRegressor,
  LassoLarsRegressor, LassoLarsCVRegressor,
  LassoLarsICRegressor, LinearRegressor,
  OrthogonalMatchingPursuitRegressor,
  OrthogonalMatchingPursuitCVRegressor,
  PassiveAggressiveRegressor, RidgeRegressor,
  RidgeCVRegressor, SGDRegressor, TheilSenRegressor

• (New feature) The macro @pipeline allows one to construct linear
  (non-branching) pipeline composite models with one line of code. One
  may include static transformations (ordinary functions) in the
  pipeline, as well as target transformations for the supervised case
  (when one component model is supervised).

• (Breaking) Source nodes (type Source) now have a kind field,
  which is either :input,:target or :other, with :input the
  default value in the source constructor. If building a learning
  network, and the network is to be exported as a standalone model,
  then it is now necessary to tag the source nodes accordingly, as in
  Xs = source(X) and ys = source(y, kind=:target).

• (Breaking) By virtue of the preceding change, the syntax for
  exporting a learning network is simplified. Do?@from_network for
  details. Also, one now uses fitresults(N) instead of fit results(N, X, y) and fitresults(N, X) when exporting a learning
  network N "by hand"; see the updated
  manual
  for details.

• (Breaking) One must explicitly state if a supervised learning
  network being exported with @from_network is probabilistic by
  adding is_probablistic=true to the macro expression. Before, this
  information was unreliably inferred from the network.

• (Enhancement) Add macro-free method for loading model code into an arbitrary
  module. Do ?load for details.

• (Enhancement) @load now returns a mode instance with default
  hyperparameters (instead of nothing), as in tree_model = @load DecisionTreeRegressor

• (Breaking) info("PCA") now returns a named-tuple, instead of a
  dictionary, of the properties of a the model named "PCA"

• (Breaking) The list returned by models(conditional) is now a list
  of complete metadata entries (named-tuples, as returned by
  info). An entry proxy appears in the list exactly when
  conditional(proxy) == true. Model query is simplified; for
  example models() do model model.is_supervised && model.is_pure_julia end finds all pure julia supervised models.

• (Bug fix) Introduce new private methods to avoid relying on MLJBase
  type piracy MLJBase
  #30.

• (Enhancement) If composite is a a learning network exported as a
  model, and m = machine(composite, args...) then report(m)
  returns the reports for each machine in the learning network, and
  similarly for fitted_params(m).

• (Enhancement) MLJ.table, vcat and hcat now overloaded for
  AbstractNode, so that they can immediately be used in defining
  learning networks. For example, if X = source(rand(20,3)) and
  y=source(rand(20)) then MLJ.table(X) and vcat(y, y) both make
  sense and define new nodes.

• (Enhancement) pretty(X) prints a pretty version of any table X,
  complete with types and scitype annotations. Do ?pretty for
  options. A wrap of pretty_table from PrettyTables.jl.

• (Enhancement) std is re-exported from Statistics

• (Enhancement) The manual and MLJ
  cheatsheet
  have been updated.

• Performance measures have been migrated to MLJBase, while the model
  registry and model load/search facilities have migrated to
  MLJModels. As relevant methods are re-exported to MLJ, this is
  unlikely to effect many users.