caretmetrics 0.1.0

caret-style confusion matrices and classification statistics for Python

caretmetrics

caret-style confusion matrices and classification statistics for Python.

Reproduces the output of R's caret::confusionMatrix() — same table orientation (Prediction in rows, Reference in columns), a chosen positive class, the exact (Clopper–Pearson) 95% CI, the Acc > NIR binomial test, McNemar's test, and Cohen's Kappa.

Install (local, editable)

From the project folder:

pip install -e .

This installs the package in "editable" mode, so edits to the source take effect immediately without reinstalling.

Usage

import pandas as pd
from caretmetrics import confusion_matrix

df = pd.read_excel("data.xlsx")
cm = confusion_matrix(df["survived"], df["pred_classes"], positive="yes")

print(cm)              # caret-style report
cm.accuracy            # 0.774...
cm.kappa               # 0.528...
cm.as_dict()           # all statistics as a dict
cm.table               # 2x2 numpy array (rows=Prediction, cols=Reference)

Notes

• Positive class is explicit; flipping it swaps sensitivity/specificity and the predictive values.
• Orientation is the opposite of sklearn.metrics.confusion_matrix, which is the usual reason Python and R matrices don't line up.

Test

pip install -e ".[dev]"
pytest

License

MIT