We’ve already written about classifying instances in Python. However, it’s always nice to have a comprehensive list of classifiers and a step-by-step procedure at hand.

TRAINING THE CLASSIFIER

We start with simply importing Orange module into Python and loading our data set.

>>>> import Orange
>>>> data = Orange.data.Table("titanic")

We are using ’titanic.tab’ data. You can load any data set you want, but it does have to have a categorical class variable (for numeric targets use regression). Now we want to train our classifier.

>>>> learner = Orange.classification.LogisticRegressionLearner()
>>>> classifier = learner(data)
>>>> classifier(data[0])

Python returns the index of the value, as usual.

array[0.]

To check what’s in the class variable we print:

>>>>print("Name of the variable: ", data.domain.class_var.name)
>>>>print("Class values: ", data.domain.class_var.values)
>>>>print("Value of our instance: ", data.domain.class_var.values[0])

Name of the variable: survived
Class values: no, yes
Value of our instance: no

PREDICTIONS

If you want to get predictions for the entire data set, just give the classifier the entire data set.

>>>> classifier(data)

array[0, 0, 0, ..., 1, 1, 1]

If we want to append predictions to the data table, first use classifier on the data, then create a new domain with an additional meta attribute and finally form a new data table with appended predictions:

svm = classifier(data)

new_domain = Orange.data.Domain(data.domain.attributes, data.domain.class_vars, [data.domain.class_var])

table2 = Orange.data.Table(new_domain, data.X, data.Y, svm.reshape(-1, 1))

We use .reshape to transform vector data into a reshaped array. Then we print out the data.

print(table2)

PARAMETERS

Want to use another classifier? The procedure is the same, simply use:

Orange.classification.<algorithm-name>()

For most classifiers, you can set a whole range of parameters. Logistic Regression, for example, uses the following:

learner = Orange.classification.LogisticRegressionLearner(**penalty**='l2', **dual**=False, **tol**=0.0001, **C**=1.0, **fit_intercept**=True, **intercept_scaling**=1, **class_weight**=None, **random_state**=None **preprocessors**=None)

To check the parameters for the classifier, use:

print(Orange.classification.SVMLearner())

PROBABILITIES

Another thing you can check with classifiers are the probabilities.

classifier(data[0], Orange.classification.Model.ValueProbs)

>>>(array([ 0.]), array([[ 1.,  0.]]))

The first array is the value for your selected instance (data[0]), while the second array contains probabilities for class values (probability for ‘no’ is 1 and for ‘yes’ 0).

CLASSIFIERS

And because we care about you, we’re giving you here a full list of classifier names:

LogisticRegressionLearner()

NaiveBayesLearner()

KNNLearner()

TreeLearner()

MajorityLearner()

RandomForestLearner()

SVMLearner()

For other learners, you can find all the parameters and descriptions in the documentation.