Evaluate classification by compiling a reportΒΆ

Specific metrics have been developed to evaluate classifier which has been trained using imbalanced data. imblearn provides a classification report similar to sklearn, with additional metrics specific to imbalanced learning problem.

Out:

pre       rec       spe        f1       geo       iba       sup

          0       0.42      0.85      0.87      0.56      0.86      0.74       123
          1       0.98      0.87      0.85      0.92      0.86      0.74      1127

avg / total       0.93      0.87      0.85      0.89      0.86      0.74      1250

# Authors: Guillaume Lemaitre <[email protected]>
# License: MIT


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

from imblearn import over_sampling as os
from imblearn import pipeline as pl
from imblearn.metrics import classification_report_imbalanced

print(__doc__)

RANDOM_STATE = 42

# Generate a dataset
X, y = datasets.make_classification(n_classes=2, class_sep=2,
                                    weights=[0.1, 0.9], n_informative=10,
                                    n_redundant=1, flip_y=0, n_features=20,
                                    n_clusters_per_class=4, n_samples=5000,
                                    random_state=RANDOM_STATE)

pipeline = pl.make_pipeline(os.SMOTE(random_state=RANDOM_STATE),
                            LinearSVC(random_state=RANDOM_STATE))

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                    random_state=RANDOM_STATE)

# Train the classifier with balancing
pipeline.fit(X_train, y_train)

# Test the classifier and get the prediction
y_pred_bal = pipeline.predict(X_test)

# Show the classification report
print(classification_report_imbalanced(y_test, y_pred_bal))

Total running time of the script: ( 0 minutes 0.722 seconds)

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