Comments (4)
GradientBoostingClassifier: I will check and compare the final result of a GradientBoostingClassifier with other combined (ensemble) classifiers like ensemble.AdaBoostClassifier
, ensemble.ExtraTreesClassifier
or ensemble.RandomForestClassifier
. If the computed results and data structure is the same, it will be simple to support and add this classifier.
CalibratedClassifierCV: The CalibratedClassifierCV is a cross validator estimator.
Probability calibration with isotonic regression or sigmoid.
See glossary entry for cross-validation estimator.
With this class, the base_estimator is fit on the train set of the cross->validation generator and the test set is used for calibration. The probabilities >for each of the folds are then averaged for prediction. In case that cv=βprefitβ >is passed to init, it is assumed that base_estimator has been fitted already >and all data is used for calibration. Note that data for fitting the classifier >and for calibrating it must be disjoint.
So it's an encapsulation of the base_estimator
. But today there is no automatic check, decapsulation and export of the base_estimator
. But I will add it like the Pipeline
, GridSearchCV
or RandomizedSearchCV
(source: Porter.py#L75-L87).
As a workaround you can try to assign the base estimator directly: Porter(clf.base_estimator)
from sklearn-porter.
Many thanks
from sklearn-porter.
Has there been any progress on this?
from sklearn-porter.
I would also be interested in a port of XGBoost, an algorithm that regularly beats the other ensemble methods. After all, the package provides a sklearn API so it sort of belongs to the sklearn model zoo. Any plans on that?
from sklearn-porter.
Related Issues (20)
- Feature Request: translator for onehot encoder
- Feature Request: Multinomial Logistic Regression
- A bug : When the version of sklearn contains character sequences like "rc1, rc2", the Porter class cannot be created. HOT 1
- RandomForestClassifier export HOT 1
- decision tree C code exported by porter have zero integrity score with custom test_data. HOT 1
- Test code, which is part of the Readme is failing HOT 2
- [Query] Is the isolation forest model for outlier detection supported now? HOT 1
- ValueError: invalid literal for int() with base 10: 'post1' on Example from Readme HOT 2
- What does embed_data do?
- [Enhancement]Background concurrent copying GC freed for sklearn model constrcutor in Java HOT 2
- [Error] Works fine with C but getting this error when ported to Java
- OSError: Windows isn't supported yet HOT 3
- Unable to check integrity score. HOT 1
- Generating probabilities instead of categorical results
- scikit-learn-0.24.1: ModuleNotFoundError: No module named 'sklearn.tree.tree' HOT 5
- Is there any plan to support RandomForestRegressor? HOT 11
- ImportError: cannot import name 'Porter' HOT 2
- Can't use port or save functions HOT 3
- ModuleNotFoundError: No module named 'sklearn_porter' HOT 1
- ModuleNotFoundError: No module named 'sklearn.tree.tree' HOT 2
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from sklearn-porter.