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|
###################################
Classification (``classification``)
###################################
.. automodule:: Orange.classification
.. index:: logistic regression
pair: classification; logistic regression
Logistic Regression
-------------------
.. autoclass:: LogisticRegressionLearner
:members:
.. index:: random forest
pair: classification; random forest
Random Forest
-------------
.. autoclass:: RandomForestLearner
:members:
.. index:: random forest (simple)
pair: classification; simple random forest
Simple Random Forest
--------------------
.. autoclass:: SimpleRandomForestLearner
:members:
.. index:: softmax regression classifier
pair: classification; softmax regression
Softmax Regression
------------------
.. autoclass:: SoftmaxRegressionLearner
:members:
.. index:: k-nearest neighbors classifier
pair: classification; k-nearest neighbors
k-Nearest Neighbors
-------------------
.. autoclass:: KNNLearner
:members:
.. index:: naive Bayes classifier
pair: classification; naive Bayes
Naive Bayes
-----------
.. autoclass:: NaiveBayesLearner
:members:
The following code loads lenses dataset (four discrete attributes and discrete
class), constructs naive Bayesian learner, uses it on the entire dataset
to construct a classifier, and then applies classifier to the first three
data instances:
>>> import Orange
>>> lenses = Orange.data.Table('lenses')
>>> nb = Orange.classification.NaiveBayesLearner()
>>> classifier = nb(lenses)
>>> classifier(lenses[0:3], True)
array([[ 0.04358755, 0.82671726, 0.12969519],
[ 0.17428279, 0.20342097, 0.62229625],
[ 0.18633359, 0.79518516, 0.01848125]])
.. _`Naive Bayes`: http://en.wikipedia.org/wiki/Naive_Bayes_classifier
.. _`scikit-learn`: http://scikit-learn.org
.. index:: SVM
pair: classification; SVM
Support Vector Machines
-----------------------
.. autoclass:: SVMLearner
:members:
.. index:: SVM, linear
pair: classification; linear SVM
Linear Support Vector Machines
------------------------------
.. autoclass:: LinearSVMLearner
:members:
.. index:: Nu-SVM
pair: classification; Nu-SVM
Nu-Support Vector Machines
--------------------------
.. autoclass:: NuSVMLearner
:members:
.. index:: classification tree
pair: classification; tree
Classification Tree
-------------------
Orange includes three implemenations of classification trees. `TreeLearner`
is home-grown and properly handles multinominal and missing values.
The one from scikit-learn, `SklTreeLearner`, is faster. Another home-grown,
`SimpleTreeLearner`, is simpler and still faster.
The following code loads iris dataset (four numeric attributes and discrete
class), constructs a decision tree learner, uses it on the entire dataset
to construct a classifier, and then prints the tree:
>>> import Orange
>>> iris = Orange.data.Table('iris')
>>> tr = Orange.classification.TreeLearner()
>>> classifier = tr(data)
>>> printed_tree = classifier.print_tree()
>>> for i in printed_tree.split('\n'):
>>> print(i)
[50. 0. 0.] petal length ≤ 1.9
[ 0. 50. 50.] petal length > 1.9
[ 0. 49. 5.] petal width ≤ 1.7
[ 0. 47. 1.] petal length ≤ 4.9
[0. 2. 4.] petal length > 4.9
[0. 0. 3.] petal width ≤ 1.5
[0. 2. 1.] petal width > 1.5
[0. 2. 0.] sepal length ≤ 6.7
[0. 0. 1.] sepal length > 6.7
[ 0. 1. 45.] petal width > 1.7
.. autoclass:: TreeLearner
:members:
.. autoclass:: SklTreeLearner
:members:
.. index:: classification tree (simple)
pair: classification; simple tree
Simple Tree
-----------
.. autoclass:: SimpleTreeLearner
:members:
.. index:: majority classifier
pair: classification; majority
Majority Classifier
-------------------
.. autoclass:: MajorityLearner
:members:
.. index:: neural network
pair: classification; neural network
Neural Network
--------------
.. autoclass:: NNClassificationLearner
:members:
.. index:: Rule induction
pair: classification; rules
CN2 Rule Induction
------------------
.. automodule:: Orange.classification.rules
.. autoclass:: CN2Learner
:members:
.. autoclass:: CN2UnorderedLearner
:members:
.. autoclass:: CN2SDLearner
:members:
.. autoclass:: CN2SDUnorderedLearner
:members:
Calibration and threshold optimization
--------------------------------------
.. automodule:: Orange.classification.calibration
.. autoclass:: ThresholdClassifier
:members:
.. autoclass:: ThresholdLearner
:members:
.. autoclass:: CalibratedClassifier
:members:
.. autoclass:: CalibratedLearner
:members:
Gradient Boosted Trees
----------------------
.. automodule:: Orange.classification.gb
.. autoclass:: GBClassifier
:members:
.. automodule:: Orange.classification.catgb
.. autoclass:: CatGBClassifier
:members:
.. automodule:: Orange.classification.xgb
.. autoclass:: XGBClassifier
:members:
.. autoclass:: XGBRFClassifier
:members:
|
