For a 0.11.0 release

[ablaom]

Make compatibility updates to MLJBase and MLJModels to effect the following changes to MLJ (see the linked release notes for links to the issues/PRs)):

• (new model) Add LightGBM models LightGBMClassifier and
  LightGBMRegressor

• (new model) Add new built-in model, ContinuousEncoder, for
  transforming all features of a table to Continuous scitype,
  dropping any features that cannot be so transformed

• (new model) Add ParallelKMeans model, KMeans, loaded with
  @load KMeans pkg=ParallelKMeans

• (mildly breaking enhancement) Arrange for the CV
  resampling strategyto spread fold "remainders" evenly among folds in
  train_test_pairs(::CV, ...) (a small change only noticeable in
  small datasets)

• (breaking) Restyle report and fitted_params for exported
  learning networks (e.g., pipelines) to include a dictionary of reports or
  fitted_params, keyed on the machines in the underlying learning
  network. New doc-strings detail the new behaviour.

• (enhancement) Allow calling of transform on machines with Static models without
  first calling fit!

• Allow machine constructor to work on supervised models that take nothing for
  the input features X (for models that simply fit a
  sampler/distribution to the target data y) (Unsupervised learning interfaces - is transformer too narrow? #51)

Also:

• (documentation) In the "Adding New Models for General Use"
  section of the manual, add detail on how to wrap unsupervised
  models, as well as models that fit a sampler/distribution to data

• (documentation) Expand the "Transformers" sections of the
  manual, including more material on static transformers and
  transformers that implement predict (Improve documentation around static transformers #393)

[ablaom]

Doc generation to fail until MLJModels 0.9.9 is merged - waiting on JuliaAI/MLJModels.jl#239

[ablaom]

Mmm. Actually, doc generation only depends on the master branch having the update (now done; release pending).