:orphan: .. _general_examples: Examples -------- General-purpose and introductory examples for the `imbalanced-learn` toolbox. .. raw:: html
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Examples showing API imbalanced-learn usage ------------------------------------------- Examples that show some details regarding the API of imbalanced-learn. .. raw:: html
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.. only:: html .. image:: /auto_examples/api/images/thumb/sphx_glr_plot_sampling_strategy_usage_thumb.png :alt: :ref:`sphx_glr_auto_examples_api_plot_sampling_strategy_usage.py` .. raw:: html
How to use sampling_strategy in imbalanced-learn
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Examples based on real world datasets ------------------------------------- Examples which use real-word dataset. .. raw:: html
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.. only:: html .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_multi_class_under_sampling_thumb.png :alt: :ref:`sphx_glr_auto_examples_applications_plot_multi_class_under_sampling.py` .. raw:: html
Multiclass classification with under-sampling
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.. only:: html .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_topic_classication_thumb.png :alt: :ref:`sphx_glr_auto_examples_applications_plot_topic_classication.py` .. raw:: html
Example of topic classification in text documents
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.. only:: html .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_outlier_rejections_thumb.png :alt: :ref:`sphx_glr_auto_examples_applications_plot_outlier_rejections.py` .. raw:: html
Customized sampler to implement an outlier rejections estimator
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.. only:: html .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_over_sampling_benchmark_lfw_thumb.png :alt: :ref:`sphx_glr_auto_examples_applications_plot_over_sampling_benchmark_lfw.py` .. raw:: html
Benchmark over-sampling methods in a face recognition task
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.. only:: html .. image:: /auto_examples/applications/images/thumb/sphx_glr_porto_seguro_keras_under_sampling_thumb.png :alt: :ref:`sphx_glr_auto_examples_applications_porto_seguro_keras_under_sampling.py` .. raw:: html
Porto Seguro: balancing samples in mini-batches with Keras
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.. only:: html .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_impact_imbalanced_classes_thumb.png :alt: :ref:`sphx_glr_auto_examples_applications_plot_impact_imbalanced_classes.py` .. raw:: html
Fitting model on imbalanced datasets and how to fight bias
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Examples using combine class methods ==================================== Combine methods mixed over- and under-sampling methods. Generally SMOTE is used for over-sampling while some cleaning methods (i.e., ENN and Tomek links) are used to under-sample. .. raw:: html
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.. only:: html .. image:: /auto_examples/combine/images/thumb/sphx_glr_plot_comparison_combine_thumb.png :alt: :ref:`sphx_glr_auto_examples_combine_plot_comparison_combine.py` .. raw:: html
Compare sampler combining over- and under-sampling
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Dataset examples ----------------------- Examples concerning the :mod:`imblearn.datasets` module. .. raw:: html
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.. only:: html .. image:: /auto_examples/datasets/images/thumb/sphx_glr_plot_make_imbalance_thumb.png :alt: :ref:`sphx_glr_auto_examples_datasets_plot_make_imbalance.py` .. raw:: html
Create an imbalanced dataset
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Example using ensemble class methods ==================================== Under-sampling methods implies that samples of the majority class are lost during the balancing procedure. Ensemble methods offer an alternative to use most of the samples. In fact, an ensemble of balanced sets is created and used to later train any classifier. .. raw:: html
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.. only:: html .. image:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_bagging_classifier_thumb.png :alt: :ref:`sphx_glr_auto_examples_ensemble_plot_bagging_classifier.py` .. raw:: html
Bagging classifiers using sampler
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.. only:: html .. image:: /auto_examples/ensemble/images/thumb/sphx_glr_plot_comparison_ensemble_classifier_thumb.png :alt: :ref:`sphx_glr_auto_examples_ensemble_plot_comparison_ensemble_classifier.py` .. raw:: html
Compare ensemble classifiers using resampling
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Evaluation examples ------------------- Examples illustrating how classification using imbalanced dataset can be done. .. raw:: html
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.. only:: html .. image:: /auto_examples/evaluation/images/thumb/sphx_glr_plot_classification_report_thumb.png :alt: :ref:`sphx_glr_auto_examples_evaluation_plot_classification_report.py` .. raw:: html
Evaluate classification by compiling a report
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.. only:: html .. image:: /auto_examples/evaluation/images/thumb/sphx_glr_plot_metrics_thumb.png :alt: :ref:`sphx_glr_auto_examples_evaluation_plot_metrics.py` .. raw:: html
Metrics specific to imbalanced learning
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Model Selection --------------- Examples related to the selection of balancing methods. .. raw:: html
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.. only:: html .. image:: /auto_examples/model_selection/images/thumb/sphx_glr_plot_validation_curve_thumb.png :alt: :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py` .. raw:: html
Plotting Validation Curves
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Example using over-sampling class methods ========================================= Data balancing can be performed by over-sampling such that new samples are generated in the minority class to reach a given balancing ratio. .. raw:: html
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.. only:: html .. image:: /auto_examples/over-sampling/images/thumb/sphx_glr_plot_illustration_generation_sample_thumb.png :alt: :ref:`sphx_glr_auto_examples_over-sampling_plot_illustration_generation_sample.py` .. raw:: html
Sample generator used in SMOTE-like samplers
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.. only:: html .. image:: /auto_examples/over-sampling/images/thumb/sphx_glr_plot_shrinkage_effect_thumb.png :alt: :ref:`sphx_glr_auto_examples_over-sampling_plot_shrinkage_effect.py` .. raw:: html
Effect of the shrinkage factor in random over-sampling
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.. only:: html .. image:: /auto_examples/over-sampling/images/thumb/sphx_glr_plot_comparison_over_sampling_thumb.png :alt: :ref:`sphx_glr_auto_examples_over-sampling_plot_comparison_over_sampling.py` .. raw:: html
Compare over-sampling samplers
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Pipeline examples ================= Example of how to use the a pipeline to include under-sampling with `scikit-learn` estimators. .. raw:: html
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.. only:: html .. image:: /auto_examples/pipeline/images/thumb/sphx_glr_plot_pipeline_classification_thumb.png :alt: :ref:`sphx_glr_auto_examples_pipeline_plot_pipeline_classification.py` .. raw:: html
Usage of pipeline embedding samplers
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Example using under-sampling class methods ========================================== Under-sampling refers to the process of reducing the number of samples in the majority classes. The implemented methods can be categorized into 2 groups: (i) fixed under-sampling and (ii) cleaning under-sampling. .. raw:: html
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.. only:: html .. image:: /auto_examples/under-sampling/images/thumb/sphx_glr_plot_illustration_tomek_links_thumb.png :alt: :ref:`sphx_glr_auto_examples_under-sampling_plot_illustration_tomek_links.py` .. raw:: html
Illustration of the definition of a Tomek link
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.. only:: html .. image:: /auto_examples/under-sampling/images/thumb/sphx_glr_plot_illustration_nearmiss_thumb.png :alt: :ref:`sphx_glr_auto_examples_under-sampling_plot_illustration_nearmiss.py` .. raw:: html
Sample selection in NearMiss
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.. only:: html .. image:: /auto_examples/under-sampling/images/thumb/sphx_glr_plot_comparison_under_sampling_thumb.png :alt: :ref:`sphx_glr_auto_examples_under-sampling_plot_comparison_under_sampling.py` .. raw:: html
Compare under-sampling samplers
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.. toctree:: :hidden: :includehidden: /auto_examples/api/index.rst /auto_examples/applications/index.rst /auto_examples/combine/index.rst /auto_examples/datasets/index.rst /auto_examples/ensemble/index.rst /auto_examples/evaluation/index.rst /auto_examples/model_selection/index.rst /auto_examples/over-sampling/index.rst /auto_examples/pipeline/index.rst /auto_examples/under-sampling/index.rst .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-gallery .. container:: sphx-glr-download sphx-glr-download-python :download:`Download all examples in Python source code: auto_examples_python.zip ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_