SMOTE (Synthetic Minority Over-sampling Technique) is an upsampling technique used in machine learning to address the class imbalance problem, which occurs when the number of instances of one class (minority class) is significantly lower than the number of instances of the other class (majority class) in a dataset. This class imbalance can lead to biased models that perform poorly on the minority class.
SMOTE works by generating synthetic samples for the minority class. It randomly selects a minority class instance and computes the k-nearest neighbors for that instance. Then, it selects one of these neighbors randomly and creates a synthetic instance along the line segment joining the selected instance and its chosen neighbor in the feature space.
By generating synthetic samples, SMOTE helps to balance the class distribution, which can improve the performance of machine learning models, particularly for classification tasks. It is commonly used in combination with other techniques such as under-sampling the majority class or using it in conjunction with cross-validation.
SMOTE is available in various libraries for machine learning in Python, such as the imbalanced-learn library:
from imblearn.over_sampling import SMOTE
# Create an instance of SMOTE
smote = SMOTE()
# Resample the dataset
X_resampled, y_resampled = smote.fit_resample(X, y)
This code snippet demonstrates how to use SMOTE to resample a dataset X with corresponding labels y to address class imbalance. After resampling, the number of instances in the minority class will be increased to match the number of instances in the majority class, resulting in a more balanced dataset.
