Describe the bug
can't find "from sklearn.cross_validation import train_test_split" in Latest version scikit-learn
To Reproduce
Steps to reproduce the behavior:

• Day1
• Step 5: Splitting the datasets into training sets and Test sets
• Can't find "from sklearn.cross_validation import train_test_split" in Latest version scikit-learn**

**Desktop (please complete the following infor

I was having a very hard time figuring out

fill = A.stack().mean()
A.add(B, fill_value=fill)

fill = 4.5. However I computed a value of 3.2 because I was taking the mean from the column of A not the DataFrame A.
This coming after the Indexing chapter where "explicit is better than implicit." I was thinking that this should be a little more explicit.

In Transformation Pipeline make class DataFrameSelector for custom transformation and call DataFrameSelector(num_attribs) it show
TypeError: object() takes no parameters
and same with CombinedAttributesAdder
i m using colab

from sklearn.base import BaseEstimator , TransformerMixin
class DataFrameSelector(BaseEstimator,TransformerMixin):
  def _init_(self,attribute_names):
  

"Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easi

Several parts of the op sec like the main op description, attributes, input and output descriptions become part of the binary that consumes ONNX e.g. onnxruntime causing an increase in its size due to strings that take no part in the execution of the model or its verification.

Setting __ONNX_NO_DOC_STRINGS doesn't really help here since (1) it's not used in the SetDoc(string) overload (s

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'm trying to visualise the graph involved when xarray writes to a netcdf file, but no visualisation is appearing.

import dask
import xarray

ds = xarray.Dataset({'a': 0})
task = ds.to_netcdf('dat.nc',compute=False)
task.compute()
print(task)
task.visualise(filename='netcdf.svg')  # This file is never produced


def inc(x):
    return x + 1

z = dask.delayed(inc)(1)

On page 501 for the first case example the text says "n-10, m=5, p=2, s=1 -> ...". I assume the first entry of the line should be "n=10" instead of "n-10".

I think it'd be nice if our docs used something like the viewcode or linkcode sphinx extensions to make it easier to view the source code when checking the API reference

i want to know whether autosklearn support multilabel task, thank you!

  File "/root/miniconda3/bin/pipeline", line 11, in <module>
    sys.exit(_main())
  File "/root/miniconda3/lib/python3.7/site-packages/cli_pipeline/cli_pipeline.py", line 5734, in _main
    _fire.Fire()
  File "/root/miniconda3/lib/python3.7/site-packages/fire/core.py", line 127, in Fire
    component_trace = _Fire(component, args, context, name)
  Fil

认领须知

提交的时候不要改动文件名称，因为文件名和原文的链接是对应的！！！

留言格式：昵称 + QQ + 章节

需要取消认领的也在此留言。

I see the code

device = ‘cuda’ if torch.cuda.is_available() else ‘cpu’

repeated often in user code. Maybe we should introduce device='auto' exactly for this case?

We have a minor documentation conflict where the :class:~yellowbrick.regressor.residuals.PredictionError directive in our documentation is not able to resolve the link to the API documentation. The issue is that we have the yellowbrick.regressor.residuals automodule in both peplot.rst and in residuals.rst so the documentation doesn't know which one to link to. The solutions that I can se

Most of the people who start out new don't find a latest feed of community hyped resources on ML and DL topics. It would be pretty good if we add a Reddit section.
If you're fine with this suggestion I'll put up a PR with the update 😄

I installed the requirements and the demo for object detection works fine.

Is there a way how I can save and reload a trained model after the task.fit call?

detector = task.fit(dataset_train,
num_trials=2,
epochs=epochs,
lr=ag.Categorical(5e-4, 1e-4),
ngpus_per_trial=1,
time_limits=time_l

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

I think it could be useful, when one wants to plot only e.g. class 1, to have an option to produce consistent plots for both plot_cumulative_gain and plot_roc

At the moment, instead, only plot_roc supports such option.

Thanks a lot

As listed in https://docs.scipy.org/doc/scipy/reference/special.html#bessel-functions, we can implement the universal functions in the list first.

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.