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src/o/r/Orange-2.7.2/docs/reference/rst/code/logreg-stepwise.py   Orange(Download)
import Orange
 
ionosphere = Orange.data.Table("ionosphere.tab")
 
lr = Orange.classification.logreg.LogRegLearner(remove_singular=1)
learners = (
  Orange.classification.logreg.LogRegLearner(name='logistic',

src/o/r/Orange-2.7.2/docs/reference/rst/code/logreg-singularities.py   Orange(Download)
import Orange
 
adult = Orange.data.Table("adult_sample")
lr = Orange.classification.logreg.LogRegLearner(adult, remove_singular=1)
 
for ex in adult[:5]:
    print ex.getclass(), lr(ex)

src/o/r/Orange-2.7.2/Orange/orng/orngLR.py   Orange(Download)
from Orange.classification.logreg import \
    dump,\
    has_discrete_values as hasDiscreteValues,\
    LogRegLearner,\
    LogRegLearner as LogRegLearnerClass,\

src/o/r/Orange-2.7.2/docs/reference/rst/code/logreg-run.py   Orange(Download)
import Orange
 
titanic = Orange.data.Table("titanic")
lr = Orange.classification.logreg.LogRegLearner(titanic)
 
# compute classification accuracy
correct = 0.0
for ex in titanic:
    if lr(ex) == ex.getclass():

src/o/r/Orange-2.7.2/docs/tutorial/rst/code/classification-models.py   Orange(Download)
import Orange
 
data = Orange.data.Table("titanic")
lr = Orange.classification.logreg.LogRegLearner(data)
print Orange.classification.logreg.dump(lr)

src/o/r/Orange-2.7.2/docs/tutorial/rst/code/classification-other.py   Orange(Download)
knn = Orange.classification.knn.kNNLearner(train, k=21)
knn.name = "k-NN"
lr = Orange.classification.logreg.LogRegLearner(train)
lr.name = "lr"
 

src/o/r/Orange-2.7.2/docs/tutorial/rst/code/classification-cv2.py   Orange(Download)
nbc = Orange.classification.bayes.NaiveLearner()
nbc.name = "nbc"
lr = Orange.classification.logreg.LogRegLearner()
lr.name = "lr"
 

src/o/r/Orange-2.7.2/docs/reference/rst/code/imputation-logreg.py   Orange(Download)
import Orange
 
lr = Orange.classification.logreg.LogRegLearner()
imputer = Orange.feature.imputation.MinimalConstructor