General-purpose and introductory examples for the imbalanced-ensemble toolbox.

The examples gallery is still under construction. Please refer to APIs for more detailed guidelines of how to use imbens.

Basic usage examples

Quick start with imbens.

Visualize an ensemble classifier

Visualize an ensemble classifier

Customize ensemble training log

Customize ensemble training log

Train and predict with an ensemble classifier

Train and predict with an ensemble classifier

Classification examples

Examples about using classification algorithms in imbens.ensemble module.

Classify class-imbalanced hand-written digits

Classify class-imbalanced hand-written digits

Plot probabilities with different base classifiers

Plot probabilities with different base classifiers

Customize cost matrix

Customize cost matrix

Use dynamic resampling schedule

Use dynamic resampling schedule

Customize resampling target

Customize resampling target

Classifier comparison

Classifier comparison

Classification with PyTorch Neural Network

Classification with PyTorch Neural Network

Dataset examples

Examples concerning the imbens.datasets module.

Generate an imbalanced dataset

Generate an imbalanced dataset

Make a dataset class-imbalanced

Make a dataset class-imbalanced

Make digits dataset class-imbalanced

Make digits dataset class-imbalanced

Evaluation examples

Examples illustrating how classification using imbalanced dataset can be done.

Evaluate classification by compiling a report

Evaluate classification by compiling a report

Metrics specific to imbalanced learning

Metrics specific to imbalanced learning

Pipeline examples

Example of how to use the a pipeline to include under-sampling with scikit-learn estimators.

Usage of pipeline embedding samplers

Usage of pipeline embedding samplers

Visualizer examples

Examples concerning the imbens.visualizer. module.

Plot confusion matrix

Plot confusion matrix

Plot performance curves

Plot performance curves