MISC: documentation and cosmits

Address comments by @jnothman
DOC no more "arithmetical order" for classes
BF: load_boston() return 13 features, but 14 feature names

Last column has the target value, hence only report first 13 names.
BUG: work aroundd Cython bug to build with clang

This commit should work around a Cython bug that makes the previous
iteration code in fast_dict.pyx impossible to compile under clang. See
https://mail.python.org/pipermail/cython-devel/2013-April/003566.html
Fixes Issue 2751
Merge pull request #2865 from hanke/boston_13features

BF: load_boston() returns 13 features, but 14 feature names
Merge branch 'master' into hc_linkage

Conflicts:
	doc/whats_new.rst
ENH Cache features value for extra trees
FIX: Removed coef and improved initialization
Merge pull request #2866 from Manoj-Kumar-S/Iss2751

Fixes Issue 2751
FIX integer dtype for labels in DBSCAN

Fixes #2833.
FIX numerical stability issue in BernoulliRBM

Fixes #2785 by replacing log1p(exp(x)) with logaddexp(0, x).
Fix typo in fast sgd classifier implementation comments
Merge pull request #2869 from jperla/master

DOC Fix typo in fast sgd classifier implementation comments
FIX: sample_weight='auto' for RidgeClassifier
Fix citation in TfidfTransformer

Corrected names order for tf-idf citation and corrected pages numbers.
See: http://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf
Merge pull request #2877 from pmandera/tfidf-citation

Fix citation in TfidfTransformer
ENH: small optimization of the classification criterion
ENH: small optimization to the regression criterion
ENH speed up RBM training with scipy.special.expit

SciPy 0.10 already has an implementation of the logistic function called
scipy.special.expit.

Using this makes RBM training 12% faster, as measured by observing the time
per iteration as reported by the plot_rbm_logistic_classification.py example,
disregarding the first iteration as an outlier (it's consistently faster than
the rest, not sure why). The speedup is in the expit function, not the
inplace operations; those are there to make benchmarking easier, but they
certainly won't hurt.