BUG: Fix performance regression on large datasets in random forest.
The crossover point seems to be around 5k rows. To get a fast predict
time on fewer than 5k rows, set n_jobs to 1 after fit and before
predict. Alternatives would be to split n_jobs into two parameters,
for fit and predict, or to have a complicated heuristic based on
n_estimators, n_samples, n_features, tree depth, etc.
DOC: Emphasize that n_jobs is for fit and predict methods in random forests.
Merge pull request #1700 from erg/rf

Speed regression in random forests for predicting > 5k rows + clarify docs for n_jobs
decision_function forces dense output (in the case of sparse coef_)
FIX: use DOUBLE_t type
Merge pull request #1705 from glouppe/tree-fix

[MRG]: FIX: use DOUBLE_t type
COSMIT pep8: blank line contains whitespace
DOC added sentence about oob_decision_function_ containing NaN to docstring. Still need some narrative about oob score.
ENH sparsify method for L1-reg linear models

Currently implemented on LinearSVC, LR and SGD.
DOC developer guidelines for unit tests and classes_
DOC add 0.13.1 changelog to whats_new.rst
DOC dev guide: random_state_ + @amueller's remarks

Fixes #1707.
DOC r2_score may return negative values
DOC dev guide: deprecation
ENH: support float value for max_features
DOC: if float, then max_features is a percentage
ENH: Defer parameter checking of trees
DOC: GBRT max_features
TEST: added test
ENH: use numbers