Htykuut

Working on an imbalanced project I was wondering what classifiers come with a class_weigth parameter out of the box.

Having been inspired by:

from sklearn.utils.testing import all_estimators



estimators = all_estimators()



for name, class_ in estimators:

     if hasattr(class_, 'predict_proba'):

     print(name) 

'compute_class_weight' is a function and not a class. So essentially I am looking for a snippet that prints any classifier that calls for compute_class_weight (to be 'balanced':-) function.

You can get the classifiers (not all estimators) and check for class_weight attribute in the instantiated objects:

from sklearn.utils.testing import all_estimators



estimators = all_estimators(type_filter='classifier')

for name, class_ in estimators:

    if hasattr(class_(), 'class_weight'): # Note the parenthesis: class_() 

        print(name)

Generates the list of the classifiers that can handle class imbalance:

DecisionTreeClassifier

ExtraTreeClassifier

ExtraTreesClassifier

LinearSVC

LogisticRegression

LogisticRegressionCV

NuSVC

PassiveAggressiveClassifier

Perceptron

RandomForestClassifier

RidgeClassifier

RidgeClassifierCV

SGDClassifier

SVC

Note that class_weight is an attribute of the instantiated models and not of the classes of the models. The class LogisticRegression doesn't have class_weight, but a model of type LogisticRegression does. This is the basic Object-Oriented distiction between an instance and a class.
You can check the difference practically with this code:

from sklearn.linear_model import LogisticRegression



logreg_class = LogisticRegression

print(type(logreg_class))

# >>> <class 'type'>



logreg_model = LogisticRegression()

print(type(logreg_model))

# >>> <class 'sklearn.linear_model.logistic.LogisticRegression'>

During the loop, class_ refers to the model class and class_() is a call to the constructor of that class, which returns an instance.