Optimizing Binary Classifiers: Tackling Unbalanced Datasets with 1% Minority Class

Photo by Kier in Sight Archives on Unsplash

Dealing with an unbalanced dataset, where one class is much rarer than the other, is a common challenge in machine learning. In this case, where you have a class distribution of 1% vs. 99%, the model might be biased towards the majority class, leading to poor performance on the minority class. Here are several strategies to handle this situation when building a binary classifier:

1. Resampling Techniques:

Undersampling:

• Randomly remove instances from the majority class to balance the class distribution. Be cautious not to remove too much data, as it may lead to information loss.

Oversampling:

• Replicate instances from the minority class or generate synthetic examples to increase its representation. Techniques like SMOTE (Synthetic Minority Over-sampling Technique) are commonly used.

from imblearn.under_sampling import RandomUnderSampler
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Assume X_train, y_train are your features and labels
X_train, X_test, y_train, y_test = train_test_split(X, y…