Random Oversampling and SMOTE
Learners will apply RandomOverSampler and SMOTE from imbalanced-learn to upsample the minority class, then evaluate whether precision-recall improves.
Why Oversampling Helps Imbalanced Models
Many classifiers learn a biased decision boundary when training data is dominated by one class — they effectively ignore the minority class. Oversampling increases the representation of the minority class in the training data, forcing the model to pay more attention to it. Two popular approaches are random oversampling (duplicating existing minority samples) and SMOTE (synthesising new ones).
Random Oversampling: Duplicate Minority Samples
RandomOverSampler from the imbalanced-learn library randomly duplicates examples from the minority class until the desired class ratio is achieved. It is simple and effective, but the duplicate samples add no new information — the model may overfit to the repeated minority examples if not regularised carefully.
from imblearn.over_sampling import RandomOverSampler
from sklearn.datasets import make_classification
import numpy as np
X, y = make_classification(n_samples=1000, weights=[0.95, 0.05],
n_features=10, random_state=42)
print('Before resampling:', np.bincount(y))
ros = RandomOverSampler(random_state=42)
X_res, y_res = ros.fit_resample(X, y)
print('After resampling: ', np.bincount(y_res))