Skip to content
FacebookTwitterLinkedinYouTubeGitHubSubscribeEmailRSS
Close
Beyond Knowledge Innovation

Beyond Knowledge Innovation

Where Data Unveils Possibilities

  • Home
  • AI & ML Insights
  • Machine Learning
    • Supervised Learning
      • Introduction
      • Regression
      • Classification
    • Unsupervised Learning
      • Introduction
      • Clustering
      • Association
      • Dimensionality Reduction
    • Reinforcement Learning
    • Generative AI
  • Knowledge Base
    • Introduction To Python
    • Introduction To Data
    • Introduction to EDA
  • References
HomeImplementationSupervised LearningOversampling Technique – SMOTE
Supervised Learning

Oversampling Technique – SMOTE

April 23, 2024April 23, 2024CEO 191 views

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.

imbalance, oversampling, smote

Post navigation

Previous Post
Previous post: Differences between Bagging and Boosting
Next Post
Next post: Undersampling Technique – Tomek Links

You Might Also Like

No image
Undersampling Technique – Tomek Links
April 23, 2024 Comments Off on Undersampling Technique – Tomek Links
  • Recent
  • Popular
  • Random
  • No image
    8 months ago Low-Rank Factorization
  • No image
    8 months ago Perturbation Test for a Regression Model
  • No image
    8 months ago Calibration Curve for Classification Models
  • No image
    March 15, 20240Single linkage hierarchical clustering
  • No image
    April 17, 20240XGBoost (eXtreme Gradient Boosting)
  • No image
    April 17, 20240Gradient Boosting
  • No image
    March 15, 2024Unsupervised Learning Dimensionality Reduction – Feature Elimination…
  • No image
    March 8, 2024Post-pruning Decision Tree with Cost Complexity Parameter…
  • No image
    March 17, 2024t-distributed Stochastic Neighbor Embedding (t-SNE)
  • Implementation (55)
    • EDA (4)
    • Neural Networks (10)
    • Supervised Learning (26)
      • Classification (17)
      • Linear Regression (8)
    • Unsupervised Learning (11)
      • Clustering (8)
      • Dimensionality Reduction (3)
  • Knowledge Base (44)
    • Python (27)
    • Statistics (6)
June 2025
M T W T F S S
 1
2345678
9101112131415
16171819202122
23242526272829
30  
« Oct    

We are on

FacebookTwitterLinkedinYouTubeGitHubSubscribeEmailRSS

Subscribe

© 2025 Beyond Knowledge Innovation
FacebookTwitterLinkedinYouTubeGitHubSubscribeEmailRSS