ImportError+cannot+import+name+joblib+from+sklearn+externals

Not specified by the original author.

TL;DR: scikit-learn 0.23 removed its vendored joblib. Change from sklearn.externals import joblib to plain import joblib (install it if missing).

The error

ImportError: cannot import name 'joblib' from 'sklearn.externals'

Fix it

1. Replace the import:

   import joblib

   Expected: no import error.

2. If joblib is not installed:

   pip install joblib

3. Verify:

   python -c "import joblib; print(joblib.__version__)"

   Expected: prints a version.

When this applies

Use this when old code or tutorials do from sklearn.externals import joblib on scikit-learn 0.23+.

When it does not apply

If the error names a different sklearn.externals member (like six), that vendored copy is gone too; find its standalone package. If sklearn itself will not import, reinstall it.

Compatibility

scikit-learn 0.23+. Plain import joblib works on every sklearn version.

Root cause

sklearn used to vendor joblib under sklearn.externals. In 0.23 the vendored copy was dropped and joblib became a proper declared dependency, so the old import path no longer exists.

Edge cases

Old pickles saved with sklearn.externals.joblib still load fine with standalone joblib. If a requirements file pins ancient sklearn, upgrading sklearn is better than working around the import.

GitHub issue mosh-hamedani/python-supplementary-materials#1 reports ImportError: cannot import name 'joblib' from 'sklearn.externals' with a modern scikit-learn. The verified fix: sklearn 0.23 removed the vendored sklearn.externals.joblib, so the import becomes plain import joblib (installing the standalone joblib package if needed), which works on all sklearn versions. Full thread: https://github.com/mosh-hamedani/python-supplementary-materials/issues/1

Not specified by the original author.

TL;DR: your yellowbrick is too old for your scikit-learn. sklearn moved safe_indexing to a private module, so upgrade yellowbrick to a compatible release, or pin an older sklearn.

The error

ImportError: cannot import name 'safe_indexing' from 'sklearn.utils'

Fix it

1. Upgrade yellowbrick:

   pip install -U yellowbrick

   Expected: installs a release that works with your sklearn.

2. Verify:

   python -c "import yellowbrick; print('ok')"

   Expected: imports cleanly.

3. If you cannot upgrade yellowbrick, pin sklearn to a version that still exports the name:

   pip install "scikit-learn<1.3"

   Expected: the old import path works again.

When this applies

Use this when import yellowbrick raises this error with a recent scikit-learn.

When it does not apply

If your own code imports safeindexing from sklearn.utils, import it from sklearn.utils.indexing instead (private API, may move again). If yellowbrick is current, reinstall it.

Compatibility

yellowbrick releases before the sklearn-compat fix; scikit-learn 1.3+ where the name left sklearn.utils.

Root cause

safeindexing was always an internal helper; sklearn moved it from sklearn.utils to the private sklearn.utils.indexing module. yellowbrick imported the public-looking path, so the move broke it at import time.

Edge cases

Pinning old sklearn can break other packages that need new sklearn; upgrading yellowbrick is the durable fix. Check the yellowbrick changelog for the exact minimum version that supports your sklearn.

GitHub issue districtdatalabs/yellowbrick#1137 reports ImportError: cannot import name 'safeindexing' from 'sklearn.utils' with a modern scikit-learn installed. The verified resolution: safeindexing moved out of sklearn.utils into a private submodule, so old yellowbrick broke at import; the fix is upgrading yellowbrick to a release compatible with the installed sklearn (or pinning an older sklearn as a workaround). Full thread: https://github.com/districtdatalabs/yellowbrick/issues/1137

Not specified by the original author.

TL;DR: your xgboost is ancient; the sklearn-style XGBClassifier was added later. Upgrade xgboost and the import works.

The error

ImportError: cannot import name 'XGBClassifier' from 'xgboost'

Fix it

1. Upgrade xgboost:

   pip install -U xgboost

   Expected: installs a modern release.

2. Verify:

   python -c "from xgboost import XGBClassifier; print('ok')"

   Expected: prints ok.

When this applies

Use this when code does from xgboost import XGBClassifier on a very old xgboost.

When it does not apply

If xgboost is already modern and the import fails, the install is corrupt; reinstall. If the error is the native-library load failure instead, that is a separate missing-.so problem.

Compatibility

xgboost 0.7+. XGBClassifier has existed for years; only truly ancient installs lack it.

Root cause

Early xgboost exposed only the low-level xgb.train API. The sklearn-compatible wrappers came later, so installs from that era have no XGBClassifier name to import.

Edge cases

Upgrading across many major versions can change default parameters; validate model metrics after the jump. If a project pins the old xgboost deliberately, the sklearn API simply is not available there.

GitHub issue dmlc/xgboost#2847 reports ImportError: cannot import name 'XGBClassifier' from 'xgboost' with an old xgboost installed. The verified fix: the scikit-learn wrapper API (XGBClassifier, XGBRegressor) was added after that old release, so upgrading xgboost to a modern version provides the names. Full thread: https://github.com/dmlc/xgboost/issues/2847