example + benchmark
explanation
make some private functions + fix public API
IForest using BaseForest base class for trees
debug + plot_iforest
classic anomaly detection datasets and benchmark
small modif
BaseBagging inheritance
shuffle dataset before benchmarking
BaseBagging inheritance
remove class label 4 from shuttle dataset
pep8 + rm shuttle.csv bench_IsolationForest.png + doc decision_function
add tests
remove comments
fetching kddcup99 and shuttle datasets
fetching kddcup99 and shuttle datasets
pep8
fetching kddcup99 and shuttle datasets
pep8
new files iforest.py and test_iforest.py
sc
alternative to pandas (but very slow)
in kddcup99.py
faster parser
sc
pep8 + cleanup + simplification
example outlier detection
clean and correct
idem
random_state added
percent10=True in benchmark
mc
remove shuttle + minor changes
sc
undo modif on forest.py and recompile cython on _tree.c
fix travis
cosmit
change bagging to fix travis
Revert "change bagging to fix travis"
This reverts commit 30ea500eb818c7a2c6ea5c3d63e75c6935aa3a35.
add max_samples_ in BaseBagging.fit to fix travis
mc
API : don't add fit param but use a private _fit + update tests + examples to avoid warning
adapt to the new structure of _tree.pyx
cosmit
add performance test for iforest
add _tree.c _utils.c _criterion.c
TST : pass on tests
remove test
relax roc-auc to fix AppVeyor
add test on toy samples
Handle depth averaging at python level
plot example: rm html add png
load_kddcup99 -> fetch_kddcup99 + doc
Take into account arjoly comments
sh -> shuffle
add decision_path code from #5487 to bench
Take into account arjoly comments
Revert "add decision_path code from #5487 to bench"
This reverts commit 46ad44ab487f4fd2728d927cbe09000330e8663e.
fix bug with max_samples != int
BernoulliNB in its present form has a hard time showing the good
features, because it isn't really a linear model (XXX fix this).
A smaller test makes this easier to reproduce for users.
It is easy to overfit on the 20newsgroups dataset, by letting
classifiers learn from metadata that commonly appears in newsgroup
texts, but would be useless for identifying topics outside of this set
of newsgroups in 1993.
For example, many classifiers will tell you that three of the most
informative features are "nntp", "posting", and "host", because the
NNTP-Posting-Host header appears with different frequency in different
groups.
The fetch_20newsgroups function now allows you to ask for any of the
following kinds of text to be removed:
- Newsgroup headers (which contain lots of NNTP metadata that can
identify the group)
- Signature blocks (which often contain multiple terms that uniquely
identify the person posting, which in turn identifies the group)
- Quote blocks (which contain people's e-mail addresses and large
amounts of text from another post in the same newsgroup)
The 20newsgroups classification example takes the "--filtered" flag,
which will remove all of these. This noticeably decreases the accuracy
of all classifiers, leaving room for a better method to improve the
accuracy.
Rémy Léone