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title: Confusion Matrix
emoji: π
colorFrom: yellow
colorTo: green
sdk: gradio
sdk_version: 3.17.0
app_file: app.py
pinned: false
tags:
- evaluate
- metric
description: >-
Compute confusion matrix to evaluate the accuracy of a classification. By
definition a confusion matrix :math:C is such that :math:C_{i, j} is equal to
the number of observations known to be in group :math:i and predicted to be in
group :math:j. Thus in binary classification, the count of true negatives is
:math:C_{0,0}, false negatives is :math:C_{1,0}, true positives is
:math:C_{1,1} and false positives is :math:C_{0,1}.
Metric Card for Confusion Matrix
Metric Description
Compute confusion matrix to evaluate the accuracy of a classification.
By definition a confusion matrix :math:C
is such that :math:C_{i, j}
is equal to the number of observations known to be in group :math:i
and
predicted to be in group :math:j
.
Thus in binary classification, the count of true negatives is
:math:C_{0,0}
, false negatives is :math:C_{1,0}
, true positives is
:math:C_{1,1}
and false positives is :math:C_{0,1}
.
How to Use
At minimum, this metric requires predictions and references as inputs.
>>> cfm_metric = evaluate.load("BucketHeadP65/confusion_matrix")
>>> results = cfm_metric.compute(references=[1, 2, 3, 2, 1, 1, 0, 2], predictions=[1, 0, 3, 2, 2, 1, 0, 3])
>>> print(results)
{'confusion_matrix': [[1, 0, 0, 0], [0, 2, 1, 0], [1, 0, 1, 1], [0, 0, 0, 1]]}
Inputs
- predictions (
list
ofint
): Predicted labels. - references (
list
ofint
): Ground truth labels. - normalize (
str
orNone
): {true
,pred
,all
}, default=None Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population. If None, confusion matrix will not be normalized - sample_weight (
list
offloat
): Sample weights Defaults to None. - labels (
list
offloat
): default=None List of labels to index the matrix. This may be used to reorder or select a subset of labels. IfNone
is given, those that appear at least once iny_true
ory_pred
are used in sorted order.
Output Values
- confusion_matrix(
list
ofint
): Confusion matrix. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, ifnormalize
is set toTrue
.. A higher score means higher accuracy. Output Example(s):
{'confusion_matrix': [[1, 0, 0, 0], [0, 2, 1, 0], [1, 0, 1, 1], [0, 0, 0, 1]]}
This metric outputs a dictionary, containing the confusion matrix.
Examples
from sklearn.metrics import confusion_matrix y_true = [2, 0, 2, 2, 0, 1] y_pred = [0, 0, 2, 2, 0, 2] confusion_matrix(y_true, y_pred) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]])
y_true = ["cat", "ant", "cat", "cat", "ant", "bird"] y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"] confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"]) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]])
In the binary case, we can extract true positives, etc as follows:
tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel() (tn, fp, fn, tp) (0, 2, 1, 1)
Citation(s)
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
Further References
Wikipedia entry for the Confusion matrix https://en.wikipedia.org/wiki/Confusion_matrix`_ (Wikipedia and other references may use a different convention for axes).