Multiclass classification with under-sampling

Some balancing methods allow for balancing dataset with multiples classes. We provide an example to illustrate the use of those methods which do not differ from the binary case.

Out:

pre       rec       spe        f1       geo       iba       sup

          0       1.00      1.00      1.00      1.00      1.00      1.00         8
          1       1.00      0.73      1.00      0.84      0.93      0.88        11
          2       0.80      1.00      0.84      0.89      0.89      0.78        12

avg / total       0.92      0.90      0.94      0.90      0.94      0.87        31

# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT

from sklearn.datasets import load_iris
from sklearn.svm import LinearSVC
from sklearn.model_selection import train_test_split

from imblearn.under_sampling import NearMiss
from imblearn.pipeline import make_pipeline
from imblearn.metrics import classification_report_imbalanced

print(__doc__)

RANDOM_STATE = 42

# Create a folder to fetch the dataset
iris = load_iris()
# Make the dataset imbalanced
# Select only half of the first class
iris.data = iris.data[25:-1, :]
iris.target = iris.target[25:-1]

X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,
                                                    random_state=RANDOM_STATE)

# Create a pipeline
pipeline = make_pipeline(NearMiss(version=2, random_state=RANDOM_STATE),
                         LinearSVC(random_state=RANDOM_STATE))
pipeline.fit(X_train, y_train)

# Classify and report the results
print(classification_report_imbalanced(y_test, pipeline.predict(X_test)))

Total running time of the script: ( 0 minutes 0.294 seconds)