sklearn-evaluation

0.12.2 verified Fri May 01 auth: no python

A Python library for evaluating scikit-learn models, providing a rich set of plots, tables, and markdown reports. Current version 0.12.2. Released irregularly; latest releases are minor patches.

pip install sklearn-evaluation

Common errors

error ModuleNotFoundError: No module named 'sklearn-evaluation' ↓

cause Trying to import the package with a hyphen instead of an underscore.

fix

Use 'import sklearn_evaluation' or 'from sklearn_evaluation import ...'

error AttributeError: module 'sklearn_evaluation' has no attribute 'plot' ↓

cause Trying to use old functional API (sklearn_evaluation.plot.confusion_matrix) after version 0.5.

fix

Use the new class-based API: from sklearn_evaluation import ConfusionMatrix; ConfusionMatrix(...).plot()

Warnings

gotcha Import from 'sklearn_evaluation' with underscores, not 'sklearn-evaluation' (hyphen). The package name on PyPI uses a hyphen, but the import uses an underscore. ↓

fix Use 'from sklearn_evaluation import ...'

deprecated Version 0.5 introduced a new API for reports, deprecating the old 'sklearn_evaluation.plot.*' functions. Users should use the new object-oriented API (e.g., ClassificationReport, ConfusionMatrix) instead of the old functional interface. ↓

fix Use classes like ClassificationReport, ConfusionMatrix, etc.

breaking In version 0.5, the report generation API changed. Old code using 'sklearn_evaluation.report' may break. ↓

fix Migrate to the new API: from sklearn_evaluation import ClassificationReport

Imports

ClassificationReport
```
from sklearn_evaluation import ClassificationReport
```
The correct import uses underscores, not hyphens.

Quickstart

Train a Random Forest classifier and generate a classification report.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn_evaluation import ClassificationReport

X, y = make_classification(random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf = RandomForestClassifier(random_state=0)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

report = ClassificationReport(y_test, y_pred)
print(report)
report.plot()