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

It may be good to provide pure Python implementation of Gradient Descent (instead of SciPy one) for Logistic Regression just for the learning purposes.

My feature request is to include an option on a button made from choice skill, to redirect a link to an external url...

Here's a detailed explanation including screenshots

https://help.botpress.io/t/how-to-redirect-to-an-external-url-while-using-a-button-made-in-choice-skill/1791

This option will be really beneficial using choice skill buttons since at the moment, you can only add an ext

i'm a newbie in programming. I try to use this library. it's very useful for me.
I want to show centroid in K-means clustering. how to show it? thank u so much..

is it Grid Search can solve CASH problems with NNI , it seems that it is usually used for hyper-parameters optimization, have you guys have finished some revision for Grid Search for solving CASH problems.

about Cash problems can refer to ：microsoft/nni#1178

The iloc in cudf documentation for 0.12 and 0.13 shows:

>>> df = DataFrame([('a', list(range(20))),
...                 ('b', list(range(20))),
...                 ('c', list(range(20)))])

https://rapidsai.github.io/projects/cudf/en/0.12.0/api.html#cudf.core.dataframe.DataFrame.iloc
https://rapidsai.github.io/projects/cudf/en/0.13.0/api.html#cudf.core.dataframe.DataFrame.iloc
which

Neither prims algorithm properly respects the gen_min_span_tree parameter. Looks like maybe a copy paste error since the algorithm does the work to compute but doesn't actually look to see if it should return the tree like the generic or boruvka do. See:

https://github.com/scikit-learn-contrib/hdbscan/blob/3fc2b6fe64def29adb26236ec69fa6701f16a139/hdbscan/hdbscan_.py#L213

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.

Current unit-tests for spectral clustering/embedding only run the code, but doesn't do any checks on the resulting clusters/embeddings! This is a testing hole and must to be fixed for a better coverage. The source file of interest is cpp/test/sg/spectral_test.cu.

Describe the bug

Various places in our docs https://stellargraph.readthedocs.io/en/latest/api.html have poor formatting:

• SampledBreadthFirstWalk.run "with replacement ..."
  ![image](https://user-images.githubusercontent.com/1203825/74402045-3aa22100-4e77-11ea-9371-232dd4bccc

Describe the bug
TimeSeriesScalerMeanVariance returns 3 dimensions, when the data matrix (N x T) is 2 dimensions.

To Reproduce

X = np.random.random((87,120))
print(X.shape)
X = TimeSeriesScalerMeanVariance().fit_transform(X)
print(X.shape)

Expected behavior
It should be squeezed, or at least better documentation.

Additional context
Happy to submit a PR

• The median heuristic function utils.medianHeuristic is missing in the pytorch branch. Please copy it to edgeml_pytorch.utils.
• It is being called from here when you run the example. That is

All of my relevant code:

#!/usr/bin/env python3.5

from data_generator import data_generator as dg

# standard imports
from keras.models import load_model
from keras.utils import to_categorical
from keras.wrappers.scikit_learn import KerasClassifier
from os import listdir
import pandas as pd
import numpy as np
from modAL.models import ActiveLearner

######## NEW STUFF ##