I tried building the docs, but was met with a graphviz error. Typically this means I can spend a few hours pecking away at the dependencies until I get stable build... or someone that has it working can export their environment, and publish an environment.yml that we can use with the build instructions.
I was going off of the d2l book since that's a dep here, but their [environment.yml](https://g

Sklearn has a TON of estimators, metrics, and cv iterators that could trivially be added to the xcessiv.presets package. I'm a bit focused on other issues to bother adding them all.

Anyone who can help add to the list can easily do so.

Adding preset estimators/metrics/cvs is very easy. There's literally no need to understand how the rest of Xcessiv works, just take a look and copy the patt

From alan-turing-institute/MLJBase.jl#68:

This doesn't work:

@mlj_model mutable struct Bar
    a::Int = -1::(_ > -2)
end

But this does:

@mlj_model mutable struct Bar
    a::Int = (-)(1)::(_ > -2)
end

This needs to be documented in MLJ/docs/src/adding_models_for_general_use and MLJ/docs/src/quick_start_guide_to_adding_models

If I understand it right, check_instances seems to tend to cluster transformers with same types without regard to the list order.
So the results from make_group and BaseEnsemble are also wrong.

Reproduce - Case 1

Code

from mlens.utils import check_instances
from sklearn.preprocessing import (
    StandardScaler as SS, FunctionTransformer as FT)
from xgboost imp

nowadays, docs with rarely interpretation is difficult to understand the algorithm,
Such as bayes rule sets and other Underdogs have little references.
If can provide a common introduce in interface level may be good for programmers
who have little information of algorithm to start

I am looking for a volunteer to convert this tutorial into Bookdown format so that the content is easier to navigate. Once it's in Bookdown, we can find a place to host it on the web.

If you are interested, please let me know! (please reply here or send me an email)

In Python XGBoost one can provide weights for each row of the data, see http://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBClassifier.fit. I tried to look for a way to specify such weights in SharpLearning, but could not find it. Is this possible?

Add a section in the documentation containing all information for development:

• Guidelines for contributing
• How to add a DS method/class
• Specific details about implementation like Label encoders