Handling Imbalanced Datasets with imbalanced-learn

In machine learning, imbalanced datasets can lead to biased models that perform poorly on minority classes. This is particularly problematic in critical applications like fraud detection or disease diagnosis.

With imbalanced-learn, you can rebalance your dataset using various sampling techniques that work seamlessly with scikit-learn.

To demonstrate this, let’s generate a sample dataset with 5000 samples, 2 features, and 4 classes:

# Libraries for plotting
import matplotlib.pyplot as plt
from mlxtend.plotting import plot_decision_regions

# Libraries for machine learning
from sklearn.datasets import make_classification
from sklearn.svm import LinearSVC
from imblearn.over_sampling import RandomOverSampler

X, y = make_classification(
    n_samples=5000,
    n_features=2,
    n_informative=2,
    n_redundant=0,
    n_repeated=0,
    n_classes=4,
    n_clusters_per_class=1,
    weights=[0.01, 0.04, 0.5, 0.90],
    class_sep=0.8,
    random_state=0,
)

Resample the dataset using the RandomOverSampler class from imbalanced-learn to balance the class distribution. This technique works by duplicating minority samples until they match the majority class.

ros = RandomOverSampler(random_state=1)
X_resampled, y_resampled = ros.fit_resample(X, y)

Plot the decision regions of the dataset before and after resampling using a LinearSVC classifier:

# Plotting Decision Regions
fig, (ax0, ax1) = plt.subplots(nrows=2, ncols=1, sharey=True, figsize=(6, 10))

for Xi, yi, ax, title in zip(
    [X, X_resampled],
    [y, y_resampled],
    [ax0, ax1],
    ["Without resampling", "Using RandomOverSampler"],
):
    clf = LinearSVC()
    clf.fit(Xi, yi)
    fig = plot_decision_regions(X=Xi, y=yi, clf=clf, legend=2, ax=ax)
    plt.title(title)

The plot reveals that the resampling process has added more data points to the minority class (green), effectively balancing the class distribution.

Link to imbalanced-learn.