I'm not sure if XGBoost s model is well calibrated with softmax. It would be nice to have a doc with various experiments including random forest, dart etc.

Hi,
I'm new to tpot but I got this error. I understand that score function can take strings, but I got the following error when using TPOTClassifier.

---

ValueError Traceback (most recent call last)
in
----> 1 tpot.score(X_test, y_test)

~/miniconda3/envs/ml

Hi,

I try to understand Deepdetect right now, starting with the Plattforms Docker container.
It looks great on pictures, but I have a hard time right now using it :)

My Problem: The docs seems to step over important points, like using JupyterLab. All examples shows the finished Custom masks, but how do I get them?

Is there something missing in the docs?

Example: https://www.deepdetec

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

Support error function and fresnel integrals in https://docs.scipy.org/doc/scipy/reference/special.html#error-function-and-fresnel-integrals, those are not universal functions may not need to be supported.

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

In order to successfully install examples using Docker I did the following changes:

• There seems to be missing step which clones mli-resources GitHub repository. Perhaps RUN git clone https://github.com/h2oai/mli-resources.git should be added to Dockerfile (I cloned repo manually).
• Jupyter refuses to start under root - consider adding --allow-root parameter: `docker run -i -t -p 888

See https://github.com/tensorflow/transform/blob/master/tensorflow_transform/mappers.py#L374

Doxygen headers have been added for all files, but function and class documentation needs to be added (starting with drishti public API).

TODO: The standard library style namespace macros (which prevent IDE indentation) are currently confusing doxygen. This needs to be fixed.

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

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