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Imbalanced Data Myths, Mistakes and Modern Solutions

Imbalanced Data

Myths, Mistakes and Modern Solutions

eBook

Class imbalance isn’t a problem—poor methodology is.

SMOTE, once a go-to solution, is frequently misapplied, introducing bias rather than solving it.

This book challenges outdated practices and provides rigorous, data-driven alternatives. We focus on selecting the right tools—threshold tuning, real costs (not class frequencies), and strategic evaluation metrics—to build models that work.

Ready to learn from mistakes, move beyond myths and master modern solutions? Let’s begin.

Bestselling Courses

Forecasting with Machine Learning

Forecast single and multiple time series with machine learning models. Implement backtesting to evaluate models before deployment.

Kishan Manani

Kishan Manani

$39.99

Feature Engineering for Time Series Forecasting

Create lag, window and seasonal features, perform imputation and encoding, extract datetime variables, remove outliers, and more.

Kishan Manani

Kishan Manani

$44.99

Machine Learning Interpretability

Explain interpretable and black box models with LIME, Shap, partial dependency plots and more.

Soledad Galli

Soledad Galli

$39.99

Machine Learning Specializations


Skill-focused course bundles. Advance your career with structured learning paths.

Advanced Machine Learning specialization

Advanced Machine Learning


Become an expert in machine learning with our 5 course bundle.


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Forecasting Specialization

Forecasting Specialization


Master time series forecasting with our specialized courses.


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Our Books


Python Feature Engineering Cookbook.

Python Feature engineering Cookbook

Feature Selection in Machine Learning Book

Feature Selection in Machine Learning


FREE Download

14 Common Feature Engineering Questions - FREE

Sole answers 14 of the most common questions students ask about feature engineering for machine learning, covering topics such as encoding, scaling, imputation, and outlier handling. It offers clear, practical answers grounded in real-world experience to help practitioners build better, more robust machine learning pipelines

Soledad Galli

Soledad Galli

FREE

7 Takes on Working with Imbalanced Data - FREE

Sole discusses three recent articles that shift the conversation around handling imbalanced datasets. These articles explore whether generating synthetic data is effective with current state-of-the-art algorithms, and examine the implications of using synthetic data on model performance and deployment.

Soledad Galli

Soledad Galli

FREE

We're the developers of Feature-engine

Feature-engine: Python Open Source Feature Engineering Library
► 300k+ downloads a month

► 2k+ stars on Github

► 60+ contributors

► Feature engineering and selection







Instructors



Soledad Galli: Data Scientist, Instructor, Open Source Developer

Soledad Galli, PhD

Lead Data Scientist


Kishan Manani, Data Scientist, Instructor

Kishan Manani, PhD

Lead Data Scientist




Dalibor Veljkovic, Data Scientist, Instructor

Dalibor Veljkovic

Lead Data Scientist





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