dask-ml make_classification keywords do not work

Supplying the following keywords to dask_ml.datasets.make_classification does not appear to have the expected result.

n_samples, n_informative, n_redundant, n_features

Take a look at the images of the correlation matrix between the features when comparing scikit-learn's make_classification to dask_ml's make_classification.
```python
import dask_ml.datasets as dask_datasets
import sklearn.datasets as sk_datasets
```

```python
def make_dataset_and_compute_correlation(func, **kwargs):
X, y = func(**kwargs)
ddf_features = dd.from_array(X)

corr = ddf_features.corr().compute()

return corr
```

```python
dask_corr = make_dataset_and_compute_correlation(dask_datasets.make_classification,
n_samples=10000, n_informative=12,
n_redundant=18, n_features=30,
chunks=100)
```

vs

```python
sk_corr = make_dataset_and_compute_correlation(sk_datasets.make_classification,
n_samples=10000, n_informative=12,
n_redundant=18, n_features=30)
```

### Dependencies

# Name Version Build Channel

appdirs 1.4.3 py36h28b3542_0

appnope 0.1.0 py36hf537a9a_0

asn1crypto 0.24.0 py36_0

attrs 18.1.0 py36_0

automat 0.7.0 py36_0

backcall 0.1.0 py36_0

blas 1.0 mkl

bleach 2.1.3 py36_0

bokeh 0.13.0 py36_0

ca-certificates 2018.03.07 0

cairo 1.14.12 hc4e6be7_4

certifi 2018.4.16 py36_0

cffi 1.11.5 py36h342bebf_0

chardet 3.0.4

click 6.7 py36hec950be_0

cloudpickle 0.5.3 py36_0

constantly 15.1.0 py36h28b3542_0

cryptography 2.2.2 py36h1de35cc_0

cycler 0.10.0 py36hfc81398_0

cytoolz 0.9.0.1 py36h1de35cc_1

dask 0.18.2 py36_0

dask-core 0.18.2 py36_0

dask-glm 0.1.0 py36_0

dask-ml 0.7.0 py36h1de35cc_0

dask-searchcv 0.2.0 py36_0

decorator 4.3.0 py36_0

distributed 1.22.0 py36_0

entrypoints 0.2.3 py36_2

expat 2.2.5 hb8e80ba_0

fontconfig 2.13.0 h5d5b041_1

freetype 2.9.1 hb4e5f40_0

fribidi 1.0.4 h1de35cc_0

gettext 0.19.8.1 h15daf44_3

glib 2.56.1 h35bc53a_0

graphite2 1.3.11 h2098e52_2

graphviz 2.40.1 hefbbd9a_2

harfbuzz 1.7.6 hb8d4a28_3

heapdict 1.0.0 py36_2

html5lib 1.0.1 py36_0

hyperlink 18.0.0 py36_0

icu 58.2 h4b95b61_1

idna 2.7 py36_0

incremental 17.5.0 py36_0

intel-openmp 2018.0.3 0

ipykernel 4.8.2 py36_0

ipython 6.4.0 py36_1

ipython_genutils 0.2.0 py36h241746c_0

ipywidgets 7.3.0 py36_0

jedi 0.12.1 py36_0

jinja2 2.10 py36_0

jpeg 9b he5867d9_2

jsonschema 2.6.0 py36hb385e00_0

jupyter_client 5.2.3 py36_0

jupyter_core 4.4.0 py36_0

jupyterlab 0.32.1 py36_0

jupyterlab_launcher 0.10.5 py36_0

kiwisolver 1.0.1 py36h0a44026_0

libcxx 4.0.1 h579ed51_0

libcxxabi 4.0.1 hebd6815_0

libedit 3.1.20170329 hb402a30_2

libffi 3.2.1 h475c297_4

libgfortran 3.0.1 h93005f0_2

libiconv 1.15 hdd342a3_7

libpng 1.6.34 he12f830_0

libsodium 1.0.16 h3efe00b_0

libtiff 4.0.9 hcb84e12_1

libxml2 2.9.8 hab757c2_1

locket 0.2.0 py36hca03003_1

markupsafe 1.0 py36h1de35cc_1

matplotlib 2.2.2 py36hbf02d85_2

mistune 0.8.3 py36h1de35cc_1

mkl 2018.0.3 1

mkl_fft 1.0.2 py36h6b9c3cc_0

mkl_random 1.0.1 py36h5d10147_1

msgpack-python 0.5.6 py36h04f5b5a_0

multipledispatch 0.5.0 py36_0

nbconvert 5.3.1 py36_0

nbformat 4.4.0 py36h827af21_0

ncurses 6.1 h0a44026_0

networkx 2.1 py36_0

notebook 5.6.0 py36_0

numpy 1.14.5 py36h648b28d_4

numpy-base 1.14.5 py36ha9ae307_4

openssl 1.0.2o h26aff7b_0

packaging 17.1 py36_0

pandas 0.23.3 py36h6440ff4_0

pandoc 2.2.1 h1a437c5_0

pandocfilters 1.4.2 py36_1

pango 1.42.1 he2d0c7e_2

parso 0.3.1 py36_0

partd 0.3.8 py36hf5c4cb8_0

pcre 8.42 h378b8a2_0

pexpect 4.6.0 py36_0

pickleshare 0.7.4 py36hf512f8e_0

pip 10.0.1 py36_0

pixman 0.34.0 hca0a616_3

plotly 3.0.0rc11

prometheus_client 0.2.0 py36_0

prompt_toolkit 1.0.15 py36haeda067_0

psutil 5.4.6 py36h1de35cc_0

ptyprocess 0.6.0 py36_0

pyasn1 0.4.3 py36_0

pyasn1-modules 0.2.2 py36_0

pycparser 2.18 py36_1

pygments 2.2.0 py36h240cd3f_0

pygraphviz 1.3 py36h1de35cc_1

pyopenssl 18.0.0 py36_0

pyparsing 2.2.0 py36_1

python 3.6.6 hc167b69_0

python-dateutil 2.7.3 py36_0

python.app 2 py36_8

pytz 2018.5 py36_0

pyyaml 3.13 py36h1de35cc_0

pyzmq 17.0.0 py36h1de35cc_3

readline 7.0 hc1231fa_4

requests 2.19.1

retrying 1.3.3

scikit-learn 0.19.1 py36hf9f1f73_0

scipy 1.1.0 py36hf1f7d93_0

send2trash 1.5.0 py36_0

service_identity 17.0.0 py36h28b3542_0

setuptools 39.2.0 py36_0

simplegeneric 0.8.1 py36_2

six 1.11.0 py36_1

sortedcontainers 2.0.4 py36_0

sqlite 3.24.0 ha441bb4_0

tblib 1.3.2 py36hda67792_0

terminado 0.8.1 py36_1

testpath 0.3.1 py36h625a49b_0

tk 8.6.7 h35a86e2_3

toolz 0.9.0 py36_0

tornado 5.0.2 py36h1de35cc_0

traitlets 4.3.2 py36h65bd3ce_0

twisted 17.5.0 py36_0

urllib3 1.23

wcwidth 0.1.7 py36h8c6ec74_0

webencodings 0.5.1 py36_1

wheel 0.31.1 py36_0

widgetsnbextension 3.3.0 py36_0

xz 5.2.4 h1de35cc_4

yaml 0.1.7 hc338f04_2

zeromq 4.2.5 h0a44026_0

zict 0.1.3 py36_0

zlib 1.2.11 hf3cbc9b_2

zope 1.0 py36_0

zope.interface 4.5.0 py36h1de35cc_0

Start at the dask_ml.datasets.make_classification entry point and reproduce the issue with the supplied n_samples, n_informative, n_redundant, n_features, and chunks values. Compare its computed feature correlation matrix with scikit-learn's make_classification; done means the keywords produce the expected result and the behavior is covered by a regression test.

Written by the indexing model from the issue text.