Working on #2963 , I see two warnings generated when building the R package using MSVC.

C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\MSBuild\Microsoft\VC\v160\Microsoft.CppBuild.targets(467,5):
warning MSB8029: The Intermediate directory or Output directory cannot reside under the Temporary directory as it could lead to issues with incremental build.

config.cpp
C:\

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

RFE/RFECV are not only feature selectors (SelectorMixin) but also classifiers/regressors (MetaEstimatorMixin), though ELI5 explain_weights doesn't support them as classifiers/regressors. The final fit of an RFE/RFECV object is a fitted estimator with either rfe.estimator_.coef_ or rfe.estimator_.feature_importances_ and in sklearn you do not usually follow up RFE/RFECV with another classifier

Version

com.microsoft.ml.spark:mmlspark_2.11:jar:0.18.1
spark= 2.4.3
scala=2.11.12

data (csv with header) https://gist.github.com/ttpro1995/69051647a256af912803c9a16040f43a

download data and save as csv file, put into folder /data/public/HIGGS/higgs.test.predictioncsv

val data = spark.read.option("header","true").option("inferSchema", "true").csv("/data/public/HIGGS

I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in generate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.

Of course, piping is a solution, but not for development in Jupyter Notebook, for example.

Is your feature request related to a problem? Please describe.
When generating generating targeted attacks the method arguments generate(x, y=None) can be confusing. In this case y usually refers to the target label for the attack, but users may accidentally put the correct label there, rendering the attack ineffective.

Describe the solution you'd like
Maybe we should change that

I run this code

import os
os.environ['is_test_suite']="True" # this is writen due to bug for multiprocessing and pickling I issued. #426 
from auto_ml import Predictor
from auto_ml.utils import get_boston_dataset
from auto_ml.utils_models import load_ml_model

# Load data
df_train, df_test = get_boston_dataset()

# Tell auto_ml which column is 'output'
# Also note columns t

• Unable to supply validation_data to a Keras CVExperiment via model_extra_params[“fit”]
• This is because HyperparameterHunter automatically sets validation_data to be the OOF data produced by the cross validation scheme
• I can imagine this would be unexpected behavior, so I’d love to hear any thoughts on how to clear this up

Note

• This issue (along with several others) was ori

Add tests for ensemble save and load. It can be done:

• by using some existing learner
• or by writing simple learner framework mockup

Is there a corresponding paper?

This article states: "We will present our solution at the IEEE International Conference on Data Science and Advanced Analytics (DSAA)". Unfortunately, I couldn't find the corresponding paper.

Bests,
Sebastian

Could you let me know how to use it in Jupyter notebook.

Should I add something to lightgbm package ?

Can u add an example