Imbalanced Learn is used in Python projects. Toolbox for imbalanced dataset in machine learning It has 6 direct runtime dependencies. Check its dependency graph on PyDeps to understand the full transitive dependency tree, reverse dependents, known CVEs, and license compatibility before installing.
Toolbox for imbalanced dataset in machine learning
imbalanced-learn declares 6 direct runtime dependencies on PyPI. Each one is resolved into the full dependency tree below:
Beyond its direct dependencies, imbalanced-learn can pull in further packages through its dependency tree. PyDeps resolves the entire chain from PyPI and deps.dev so you can see every transitive (nested) dependency of imbalanced-learn, expand any node on demand, and understand the full set of code that ships when you run pip install imbalanced-learn.
PyDeps checks imbalanced-learn and every package in its dependency tree against the OSV vulnerability database in real time. For each CVE you can see the severity, the affected version ranges, and the first fixed version, so you know exactly which imbalanced-learn version is safe to install before you ship.
imbalanced-learn is distributed under the MIT License. PyDeps also shows the license of every dependency in the tree so you can audit license compatibility across your whole imbalanced-learn install, not just the top-level package.
Install from PyPI with pip install imbalanced-learn. For offline or air-gapped environments, PyDeps can download imbalanced-learn together with every resolved dependency as wheel files in a single bundle, matched to your target Python version and operating system.
Switch to the dependents view to see the reverse dependencies of imbalanced-learn — the Python packages that list imbalanced-learn as a requirement. Reverse dependencies are a strong signal of how widely a package is trusted and how disruptive a breaking change would be.