An Introduction To Imbalanced Classification In Machine Learning

Machine learning models are usually treated as being balanced, i.e., the classes in the target variable are distributed about equally. However, in real-world scenarios, we often encounter imbalanced datasets, where the classes are unequally distributed. An imbalanced classification problem is an example of a classification problem where the classes are not represented equally.

Imbalanced Classification

Let’s understand this with an obvious example: fraud detection. Fraud transactions are rare compared to normal transactions, leading to an imbalanced problem. If we use this imbalanced data for our machine learning model, the model will likely overfit to the majority class whilst ignoring the minority class, due to lack of sufficient data.

Handling Imbalanced Class

There are three methods typically used to balance classes:

1. Under-sampling: The simplest way to handle the imbalance problem is by undersampling, where we undersample the majority class to match the minority class.
2. Over-sampling: In oversampling, we oversample the minority class to match the majority class.
3. SMOTE (Synthetic Minority Over-sampling Technique): This method is a type of oversampling but here we increase the minority class by creating 'synthetic' examples rather than by oversampling with replacement.

In this blog, let's focus on implementing the SMOTE technique using Python and the imbalanced-learn library.

from imblearn.over_sampling import SMOTE
from sklearn.datasets import load_breast_cancer
from collections import Counter

# Loading the dataset
data = load_breast_cancer(as_frame=True)
X, y = data.data, data.target

# Counting the class before applying SMOTE
counter = Counter(y)
print(f'Before SMOTE: {counter}')

# Applying SMOTE
smote = SMOTE()
X_smote, y_smote = smote.fit_resample(X, y)

# Counting the class after applying SMOTE
counter = Counter(y_smote)
print(f'After SMOTE: {counter}')

Before the execution of SMOTE, our datasets’ distribution was imbalanced with the classes not evenly distributed. After applying SMOTE, we notice that our data are distributed evenly, allowing a machine learning model to be effectively trained on our now balanced dataset.

Remember while dealing with imbalanced data, these methods may help in improving model performance but bear in mind that there’s no sure shot way of deciding which method will work best for your dataset. This requires comprehensive knowledge about your data and the problem that you’re working on.

Stay tuned for more such interesting topics on data science and machine learning!