scikit-learn model evaluation made easy: plots, tables and markdown reports.
Support for Python 3 only.
pip install sklearn-evaluationplot moduleGenerate evaluation plots with a single function call.
from sklearn_evaluation import plot
# code for data loading and model training
plot.confusion_matrix(y_true, y_pred, target_names=target_names)
table moduleGenerate good looking tables from your model results.
from sklearn_evaluation import table
# code for data loading and training
table.feature_importances(model)+-----------+--------------+-----------+
| name | importance | std |
+===========+==============+===========+
| Feature 0 | 0.250398 | 0.0530907 |
+-----------+--------------+-----------+
| Feature 1 | 0.232397 | 0.0523836 |
+-----------+--------------+-----------+
| Feature 2 | 0.148898 | 0.0331814 |
+-----------+--------------+-----------+
| Feature 3 | 0.0553634 | 0.0128296 |
+-----------+--------------+-----------+
| Feature 8 | 0.05401 | 0.0122248 |
+-----------+--------------+-----------+
| Feature 5 | 0.053878 | 0.01289 |
+-----------+--------------+-----------+
| Feature 6 | 0.0525828 | 0.0130225 |
+-----------+--------------+-----------+
| Feature 9 | 0.0510197 | 0.0129436 |
+-----------+--------------+-----------+
| Feature 7 | 0.0509633 | 0.0117197 |
+-----------+--------------+-----------+
| Feature 4 | 0.0504887 | 0.012844 |
+-----------+--------------+-----------+Also, running this in Jupyter will generate a pandas-like output.
A simplified API is available by packing the results of your estimator in the ClassifierEvaluator class.
from sklearn_evaluation import ClassifierEvaluator
# code for data loading and model training
ce = ClassifierEvaluator(classifier, y_test, y_pred, y_score,
feature_list, target_names)
# this plots the confusion matrix
ce.confusion_matrix()Generate reports using Markdown templates.
ce.make_report()The code above will generate a report like this one.
Reports are self-contained, all images are included in the html file using base64.