Skll

- Added AdaBoost and KNeighbors classifiers and regressors (finally closing 7).
- Added support for [kernel approximation samplers](http://scikit-learn.org/stable/modules/kernel_approximation.html). (Thanks nineil)
- All linear models are now supported by `print_model_weights` (issue 119).
- Added `f1_score_weighted` metric so that weighted F1 will be calculated even for binary classification tasks.
- Modified `f1_score_micro` and `f1_score_macro` to also always return average for binary classification tasks (instead of previous behavior where only performance on positive class was returned).

This release includes a long-standing request being finally fulfilled (part of 7). We now support Stochastic Gradient Descent!

Full changelog:
- Added support for `SGDClassifier` and `SGDRegressor`
- Added option to use FeatureHasher instead of DictVectorizer to make learning with feature sets that have millions of features possible.
- Minor documentation fix for `generate_predictions`.

All the credit for this release goes to nineil. Thanks Nils!

- Added `compute_eval_from_predictions` utility for computing evaluation metrics after experiments have been run.
- Made rounding consistent in Python 2 `kappa` code use banker's rounding, just like Python 3 does.
- Added support for printing model weights for linear SVR (110)
- Made `print_model_weights` only print all negative or all positive weights (105)
- Little PEP8 and documentation tweaks.

Fixed issue where some models would be different depending on order of feature files specified in config file (101).

- Add `--resume` option to `run_experiment` for resuming large experiments in the event of a crash.
- Fix issue where `grid_scores` was undefined when using `--keep-models`.
- Automatically generated feature set names now have sorted features to ensure they will always be generated in the same fashion.

Fixed a bug where command-line scripts didn't work after previous release. (This should hopefully be the last of these rapid fire releases. We will add unit tests for these in the future.)