Class imbalance

As explained in Statistics Every Programmer Needs

Class imbalance is a condition in which classes in a labeled dataset have substantially unequal representation.

>>> from sklearn.metrics import confusion_matrix,
>>> conf_matrix = confusion_matrix(y_test, y_pred)
>>> print(conf_matrix)
[[71 67]
 [36 91]]

How Statistics Every Programmer Needs covers it

How deep it goes

Defines majority-class bias and demonstrates class-specific accuracy, but stops short of prescribing imbalance remedies.

Examples

Uses fourth-down conversion predictions, confusion matrices, and separate accuracy calculations for labels 0 and 1.

What it assumes

Assumes labeled classification data and basic familiarity with predictions, true labels, and confusion-matrix cells.

What you can do after

Evaluate classification performance by class using confusion matrices and class-specific accuracy calculations.

How it connects

Connects class imbalance to majority class, minority class, confusion matrix, accuracy, decision trees, and random forests.

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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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  Ensemble Methods for Machine LearningClass imbalance is a data-distribution condition in which the classes contain substantially different numbers of examples.
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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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  Inside Deep Learning: Math, Algorithms, ModelsA dataset condition in which some classes occur much more frequently than others.
• 

  liveBookmanning.com
  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 Statistics Every Programmer Needs. The section links point to the parts of the book it draws on.