The comment in a few example plots refer to "the mesh [x_min, m_max] x
[y_min, y_max]" when it should be "x_max".
Also fixed the spacing in plot_random_forest_embedding.py `[x_min,
x_max] x [y_min, y_max]` to `[x_min, x_max]x[y_min, y_max]` to comply
with the coding style of other files with similar code (like those in
the current pull request).
Register OneVsRestRegressor as meta estimator
Rename to a more sensible name
Parallel predict and sparse support
Started MultiOutput documentation
Move code to new file multioutput.py
Continuing the move to new multioutput module
Added sample weight support
Better test for sample weights and actually support weights
Added a new test using weighted vs repeated samples to
test sample weight support. Uncovered that weights
were not actually passed on to underlying estimator.
Comment on multiprocess overheads
Move parallel_helper to utils.fixes
This helper works around a python2 limitation on pickling
instance methods
Example of multi-output regression with gradient boosting
Switch to uniform weighted score and updated example
The example now uses a RF with and without the MultiOutput
meta estimator
Added note for removing `score` method
Addressing comments on MultiOutputRegressor
MultiOutputregressor better test for weighted samples
Fix ups
Use explicit keyword argument for passing sample weights and
fix random_state on train-test split in the example
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
In previous commit, I used n_features to set the number of features, and plotted based on that variable. Based on feedback, I removed n_features, and now plotting is based on X.shape[1]. This makes the code general and easy to port. I copied this into an iPython notebook to verify it still outputs the correct graph.
I ported this code into my own, and when I tried plotting, I found the number of features was hard coded to 10. By switching to a set variable, the number of features is no longer a magic number, and the code is more readable.
This example trains several tree based ensemble methods and uses
them to transform the data into a high dimensional, sparse space.
The trains a linear model on this new feature space. The idea is
taken from:
Practical Lessons from Predicting Clicks on Ads at Facebook Junfeng Pan,
He Xinran, Ou Jin, Tianbing XU, Bo Liu, Tao Xu, Yanxin Shi, Antoine
Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Quiñonero Candela
International Workshop on Data Mining for Online Advertising (ADKDD)
https://www.facebook.com/publications/329190253909587/
A number of further amendments to the plot_ensemble_oob.py example
script were suggested in the PR thread and addressed accordingly:
- The ExtraTreesClassifier models were removed from the example, since
they don't use bootstrapping by default (but can be using bootstrap=True).
- Included the OOB errors for RandomForestClassifier models with various
max_features values.
- Changed the sample datasets to make for a nicer looking plot.
- Changed "cross-validated" to "validated" in the docstring.
- Added the relevant page numbers to the Hastie et al. reference.
- PEP8 compliance, fixed line > 80 chars.
@amueller provided feedback on improving my original PR (#4665) of the
plot_ensemble_oob.py script.
A number of major changes were made accordingly:
- Used `matplotlib.pyplot` instead of `pylab`.
- To improve the run-time to <10secs, I reduced the dimensionality of
the sample dataset and set the max. number of estimators to 150.
- To avoid OOB warnings, the min. number of estimators was set to 15.
Values <15 would still raise the warnings.
- The script is PEP8-compliant via the `pep8` command-line script. I
needed to move `print(__doc__)` and author list comments.
- Re-added @amueller to the author list (had mistakenly been removed).
- Added a link to this example to the user-guide under the `Ensemble
Methods` section.
Yen