Class imbalance

As explained in Ensemble Methods for Machine Learning

Class imbalance is a data-distribution condition in which the classes contain substantially different numbers of examples.

Class imbalance occurs when classes contain substantially different numbers of examples. A classifier can then achieve high accuracy while overlooking the minority class, so evaluation and training should reflect the real cost of errors. section 5.4.3 section 9.3.1

Handle the condition by aligning model behavior and evaluation with the important errors:

• Measure balanced accuracy when class frequencies are skewed, weighting predictions by class size rather than allowing the majority class to dominate. section 9.3.1
• Use recall when false negatives are costly, such as missing customers likely to subscribe or missing medical diagnoses. section 5.4.3 section 9.3.1
• Use precision when false positives are costly, such as incorrectly flagging messages as spam. section 5.4.3
• Tailor the loss when standard cross-entropy does not express the task’s priorities; focal loss emphasizes harder examples and downplays easy ones. section 5.4.3

Bank marketing prediction

Only 10% of customers subscribed to a fixed-term deposit, creating a strongly imbalanced target. Stratified splitting preserves the class ratio, while balanced accuracy and recall provide more informative evaluation than ordinary accuracy alone. Removing the post-call duration feature also avoids target leakage in a deployable model. section 9.2.1 section 9.3.1

Figure 5.21 The focal loss visualized for various values of γ. When γ = 0, the original cross-entropy loss is recovered. As γ increases, the part of the curve corresponding to well-classified examples becomes longer, reflecting the loss function’s focus on poor classification.

Visualizes how increasing focal-loss gamma shifts emphasis away from well-classified examples and toward difficult cases. section 5.4.3

How Ensemble Methods for Machine Learning covers it

How deep it goes

Explains the distributional problem, error tradeoffs, imbalance-aware metrics, and focal-loss implementation with gradient boosting.

Examples

Uses dense object detection, medical diagnosis, spam detection, and Bank Marketing subscription prediction as concrete cases.

What it assumes

Assumes binary classification and basic error terminology while reinforcing precision, recall, accuracy, and false-positive or false-negative tradeoffs.

What you can do after

Apply it via balanced accuracy, recall, stratified splitting, and focal loss to build or evaluate models under skewed class frequencies.

How it connects

Connects class imbalance to precision, recall, balanced accuracy, cross-entropy loss, focal loss, gradient boosting, and target leakage.

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Also explained in

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  Machine Learning System DesignA condition in which the frequencies of classes in a classification dataset or population differ substantially.
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  Financial AI in PracticeA condition in which a classification dataset has substantially different numbers of observations in its classes.
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  Grokking StatisticsAn imbalanced dataset has substantially unequal numbers of observations across its classes.
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  Graph Neural Networks in ActionClass imbalance is a property of a classification dataset in which the classes contain substantially different numbers of examples.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIImbalanced data has substantially unequal representation of classes or outcome groups.
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  Deep Learning with PyTorch, Second EditionA dataset in which class frequencies are heavily skewed toward one class.
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  Graph Algorithms for Data ScienceClass imbalance is a condition in which classes in a classification dataset occur with substantially unequal frequencies.
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  Statistics Every Programmer NeedsClass imbalance is a condition in which classes in a labeled dataset have substantially unequal representation.
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  Inside Deep Learning: Math, Algorithms, ModelsA dataset condition in which some classes occur much more frequently than others.
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  Outlier Detection in PythonClass imbalance is a distribution in which one or more target classes contain far fewer observations than another class.

Compare how 11 books explain class imbalance →

Related concepts

• class reshufflingaddressed by · A class-balancing technique that redistributes or resamples training examples to improve class representation without generating synthetic data.
• accuracycontrasts with · Accuracy is the degree to which an output, prediction, or measurement agrees with a correct or reference outcome under a specified criterion.
• SMOTEaddressed by · SMOTE is an oversampling technique that generates synthetic examples for a minority class by interpolating between existing minority-class examples and their nearest neighbors.
• cost-sensitive learningaddressed by · Cost-sensitive learning incorporates unequal costs for different prediction errors into model training or decision-making.

This explainer was generated by Manning's knowledge graph from the text of Ensemble Methods for Machine Learning. The section links point to the parts of the book it draws on.