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  • Getting Started
  • User Guide
  • API reference
  • Examples
  • Release history
  • About us
  • GitHub

Section Navigation

  • Examples showing API imbalanced-learn usage
    • How to use sampling_strategy in imbalanced-learn
  • Examples based on real world datasets
    • Multiclass classification with under-sampling
    • Example of topic classification in text documents
    • Customized sampler to implement an outlier rejections estimator
    • Benchmark over-sampling methods in a face recognition task
    • Porto Seguro: balancing samples in mini-batches with Keras
    • Fitting model on imbalanced datasets and how to fight bias
  • Examples using combine class methods
    • Compare sampler combining over- and under-sampling
  • Dataset examples
    • Create an imbalanced dataset
  • Example using ensemble class methods
    • Bagging classifiers using sampler
    • Compare ensemble classifiers using resampling
  • Evaluation examples
    • Evaluate classification by compiling a report
    • Metrics specific to imbalanced learning
  • Model Selection
    • Plotting Validation Curves
  • Example using over-sampling class methods
    • Sample generator used in SMOTE-like samplers
    • Effect of the shrinkage factor in random over-sampling
    • Compare over-sampling samplers
  • Pipeline examples
    • Usage of pipeline embedding samplers
  • Example using under-sampling class methods
    • Illustration of the definition of a Tomek link
    • Sample selection in NearMiss
    • Compare under-sampling samplers
  • Examples
  • Examples showing API imbalanced-learn usage

Examples showing API imbalanced-learn usage#

Examples that show some details regarding the API of imbalanced-learn.

How to use sampling_strategy in imbalanced-learn

How to use sampling_strategy in imbalanced-learn

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How to use sampling_strategy in imbalanced-learn
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