```
`).
.. literalinclude:: code/logreg-run.py
Result::
Classification accuracy: 0.778282598819
class attribute = survived
class values =
```
Attribute beta st. error wald Z P OR=exp(beta)
Intercept -1.23 0.08 -15.15 -0.00
status=first 0.86 0.16 5.39 0.00 2.36
status=second -0.16 0.18 -0.91 0.36 0.85
status=third -0.92 0.15 -6.12 0.00 0.40
age=child 1.06 0.25 4.30 0.00 2.89
sex=female 2.42 0.14 17.04 0.00 11.25
The next examples shows how to handle singularities in data sets
(:download:`logreg-singularities.py ```
`).
.. literalinclude:: code/logreg-singularities.py
The first few lines of the output of this script are::
<=50K <=50K
<=50K <=50K
<=50K <=50K
>50K >50K
<=50K >50K
class attribute = y
class values = <>50K, <=50K>
Attribute beta st. error wald Z P OR=exp(beta)
Intercept 6.62 -0.00 -inf 0.00
age -0.04 0.00 -inf 0.00 0.96
fnlwgt -0.00 0.00 -inf 0.00 1.00
education-num -0.28 0.00 -inf 0.00 0.76
marital-status=Divorced 4.29 0.00 inf 0.00 72.62
marital-status=Never-married 3.79 0.00 inf 0.00 44.45
marital-status=Separated 3.46 0.00 inf 0.00 31.95
marital-status=Widowed 3.85 0.00 inf 0.00 46.96
marital-status=Married-spouse-absent 3.98 0.00 inf 0.00 53.63
marital-status=Married-AF-spouse 4.01 0.00 inf 0.00 55.19
occupation=Tech-support -0.32 0.00 -inf 0.00 0.72
If :obj:`remove_singular` is set to 0, inducing a logistic regression
classifier returns an error::
Traceback (most recent call last):
File "logreg-singularities.py", line 4, in
```
lr = classification.logreg.LogRegLearner(table, removeSingular=0)
File "/home/jure/devel/orange/Orange/classification/logreg.py", line 255, in LogRegLearner
return lr(examples, weightID)
File "/home/jure/devel/orange/Orange/classification/logreg.py", line 291, in __call__
lr = learner(examples, weight)
orange.KernelException: 'orange.LogRegLearner': singularity in workclass=Never-worked
The attribute variable which causes the singularity is ``workclass``.
The example below shows how the use of stepwise logistic regression can help to
gain in classification performance (:download:`logreg-stepwise.py ```
`):
.. literalinclude:: code/logreg-stepwise.py
The output of this script is::
Learner CA
logistic 0.841
filtered 0.846
Number of times attributes were used in cross-validation:
1 x a21
10 x a22
8 x a23
7 x a24
1 x a25
10 x a26
10 x a27
3 x a28
7 x a29
9 x a31
2 x a16
7 x a12
1 x a32
8 x a15
10 x a14
4 x a17
7 x a30
10 x a11
1 x a10
1 x a13
10 x a34
2 x a19
1 x a18
10 x a3
10 x a5
4 x a4
4 x a7
8 x a6
10 x a9
10 x a8
..
```