Imblearn RandomOverSampler - Python

The Imblearn RandomOverSampler in Python is a data augmentation technique that helps to address imbalanced class distribution problems in classification problems. It is a preprocessing step that can be used to balance the class distribution by randomly oversampling minority classes.

How it Works

The Imblearn RandomOverSampler works by randomly duplicating samples from the minority class until the class distribution is balanced. This can be very useful in situations where there is a class imbalance and the minority class has a limited number of samples. By duplicating samples from the minority class, we can achieve a more balanced data distribution and improve the performance of our classification algorithm.

Code Example

Here is an example of how to use the Imblearn RandomOverSampler in Python:

from imblearn.over_sampling import RandomOverSampler
from sklearn.datasets import make_classification

# Generate some imbalanced data
X, y = make_classification(n_classes=2, class_sep=2,
 weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
 n_features=20, n_clusters_per_class=1, n_samples=1000,
 random_state=10)

# Use the RandomOverSampler to balance the data
ros = RandomOverSampler(random_state=42)
X_resampled, y_resampled = ros.fit_resample(X, y)

Conclusion

The Imblearn RandomOverSampler in Python is a useful technique for addressing imbalanced class distribution problems. It is a simple preprocessing step that can be used to balance the class distribution by oversampling minority classes. By using the Imblearn RandomOverSampler, we can achieve a more balanced data distribution and improve the performance of our classification algorithm.