Py's imbalanced-ensemble: a detailed guide to the introduction, installation, and use of the imbalanced-ensemble library
Table of contents
Introduction to the imbalanced-ensemble library
Installation of imbalanced-ensemble library
How to use the imbalanced-ensemble library
Introduction to the i mbalanced-ensemble library
imbalanced-ensemble is a Python library for dealing with imbalanced datasets, which provides a series of machine learning methods and tools to solve the problem of imbalanced datasets. This library aims to improve classification performance on imbalanced datasets by combining several different classifiers.
The main features of imbalanced-ensemble include:
>>Diversity: Imbalanced-ensemble provides a variety of different classifiers and ensemble methods, including sampling-based methods (such as random undersampling, random oversampling, SMOTE, etc.), cost-sensitive methods, integration-based methods (such as ensemble learning, adaptive ensemble learning, etc.), so that users can choose the appropriate method according to the characteristics and needs of the data set.
>>Scalability: imbalanced-ensemble supports integration with common machine learning libraries and frameworks such as Scikit-learn and XGBoost, and can be easily combined and used with other machine learning algorithms and tools.
>>Flexibility: imbalanced-ensemble provides a variety of parameter adjustment and customization options, so that users can adjust and customize according to different application scenarios and needs.
Installation of the i mbalanced-ensemble library
pip install imbalanced-ensemble
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple imbalanced-ensemble
How to use the i mbalanced-ensemble library
1. Basic Usage
# Train an SPE classifier
from imbens.ensemble import SelfPacedEnsembleClassifier
clf = SelfPacedEnsembleClassifier(random_state=42)
clf.fit(X_train, y_train)
# Predict with an SPE classifier
y_pred = clf.predict(X_test)