* Added predict method to class OneClassSVM
- This method overrides the default behaviour to only return integer class values.
* Test for the change in OneClassSVM predict method
- small addition to test that the predicted result is integer
* Changed test to check for intp datatype
* Updated whats new
* remove stuff to be removed 0.19
* more changes
* remove classes from 0.19 whatsnew
* remove _LearntSelectorMixin
* remove ProjectedGradientNMF, load_lwf_*
* minor fixes
* remove more copy from logistic regression path
* remove lda, qda from __init__.__all__
* remove pg solver in nmf from tests etc
* remove class_weight="auto" from tests
* doctest change for decision_function_shape="ovr"
* remove transfrom from tree test, minor fixes to tree tests
* some fixes in the tests
* undo changes in functions which still allow 1d input...
* also allow 1d in scale
* more test fixes...
* last test fixes in forest and tree
* svm default value change doctest failures
* pep8
* remove more class_weight="auto" stuff
* minor cosmetics in docstrings deprecated / removed behavior.
* say that store_covariance has been moved to __init__ in discriminant_analysis
* MNT Removing more deprecated stuff for v0.18
* MNT+TST Remove support for loss='l2'
* MNT Remove support for precompute='auto' for ElasticNet
* FIX/TST precompute=auto should raise a generic message
* Make KernelCenterer a _pairwise operation
Replicate solution to 9a520779c2 except that `_pairwise` should always be `True` for `KernelCenterer` because it's supposed to receive a Gram matrix. This should make `KernelCenterer` usable in `Pipeline`s.
Happy to add tests, just tell me what should be covered.
* Adding test for PR #6900
* Simplifying imports and test
* updating changelog links on homepage (#6901)
* first commit
* changed binary average back to macro
* changed binomialNB to multinomialNB
* emphasis on "higher return values are better..." (#6909)
* fix typo in comment of hierarchical clustering (#6912)
* [MRG] Allows KMeans/MiniBatchKMeans to use float32 internally by using cython fused types (#6846)
* Fix sklearn.base.clone for all scipy.sparse formats (#6910)
* DOC If git is not installed, need to catch OSError
Fixes#6860
* DOC add what's new for clone fix
* fix a typo in ridge.py (#6917)
* pep8
* TST: Speed up: cv=2
This is a smoke test. Hence there is no point having cv=4
* Added support for sample_weight in linearSVR, including tests and documentation
* Changed assert to assert_allclose and assert_almost_equal, reduced the test tolerance
* Fixed pep8 violations and sampleweight format
* rebased with upstream
and fixes a few other doc problems while I was at it.
The main take-home message is to remember that you need a backslash when
your parameter type description exceeds one line.
the names are now consistent across methods
In LinearSVC:
'l1' -> 'hinge'
'l2' -> 'squared_hinge'
In LinearSVR:
'l1' -> 'epsilon_insensitive'
'l2' -> 'squared_epsilon_insensitive'
LogisticRegression and liblinear's predictions differ when the
decision function is zero. Explain why and what to do in that case.
Fixes#3600 (by documenting the won't fix status).
1. Use a single loop for both ovr and multinomial conditions
2. Better warning messages for penalty and loss conditions
3. Convergence warnings print only if verbose > 0
4. Used check_X_y instead of check_X
Guillaume Lemaitre