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 nick,
Could you provide instructions for using the GRT in android? The instructions you provided on blog post is not available now.

• I'm submitting a ...
  [/] enhancement

• Summary
  As a result of upgrading the Tensorflow version to 0.15.1, we should refactor all the dataSycn with arraySync. This will greatly improve the overall readability of the code.

I ran a regression_forest for > 10 minutes and had no idea if it would complete in 15 min or an hour.

It would be great to have an argument "verbose" (default FALSE) which causes the function to
print the function's progress, to help the user estimate the remaining time before completion.

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)

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

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

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?

A few requirements like to change some variable name or check variable list in existing tree model spec, we can add some tutorial for user to check and validate such model spec or add it into Shifu as some command.

Tree model spec is a binary model and is hart to edit and check.

We should add BART as one final algorithm after xgboost and before SuperLearner - it's usually the best single model and it would be helpful to explain it to people.