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metadata
title: Confusion Matrix
emoji: 🤗
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
tags:
- evaluate
- metric
description: >-
The confusion matrix evaluates classification accuracy.
Each row in a confusion matrix represents a true class and each column
represents the instances in a predicted class.
Metric Card for Confusion Matrix
Metric Description
The confusion matrix evaluates classification accuracy. Each row in a confusion matrix represents a true class and each column represents the instances in a predicted class. Let's look at an example:
setosa | versicolor | virginica | |
---|---|---|---|
setosa | 13 | 0 | 0 |
versicolor | 0 | 10 | 6 |
virginica | 0 | 0 | 9 |
What information does this confusion matrix provide?
- All setosa instances were properly predicted as such (true positives).
- The model always correctly classifies the setosa class (there are no false positives).
- 10 versicolor instances were properly classified, but 6 instances were misclassified as virginica.
- All virginica insances were properly classified as such.
How to Use
At minimum, this metric requires predictions and references as inputs.
>>> confusion_metric = evaluate.load("confusion_matrix")
>>> results = confusion_metric.compute(references=[0, 1, 1, 2, 0, 2, 2], predictions=[0, 2, 1, 1, 0, 2, 0])
>>> print(results)
{'confusion_matrix': [[2, 0, 0], [0, 1, 1], [1, 1, 1]]}
Inputs
- predictions (
list
ofint
): Predicted labels. - references (
list
ofint
): Ground truth labels. - labels (
list
ofint
): List of labels to index the matrix. This may be used to reorder or select a subset of labels. - sample_weight (
list
offloat
): Sample weights. - normalize (
str
): Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population.
Output Values
- confusion_matrix(
list
oflist
ofstr
): Confusion matrix. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, ifnormalize
is set toTrue
.
Output Example(s):
{'confusion_matrix': [[2, 0, 0], [0, 1, 1], [1, 1, 1]]}
This metric outputs a dictionary, containing the confusion matrix.
Examples
Example 1 - A simple example
>>> confusion_metric = evaluate.load("confusion_matrix")
>>> results = confusion_metric.compute(references=[0, 1, 1, 2, 0, 2, 2], predictions=[0, 2, 1, 1, 0, 2, 0])
>>> print(results)
{'confusion_matrix': [[2, 0, 0], [0, 1, 1], [1, 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}
}