Confusion Matrix#
Guide: True Positive / False Positive / False Negative / True Negative
You can find more info on true positive, false positive, false negative, and true negative in the TP / FP / FN / TN guide.
A confusion matrix is a structured plot describing classification model performance as a table that highlights counts of objects with predicted classes (columns) against the actual classes (rows). Each cell has a count of the number of objects that have its correct class and predicted class, which indicates how confused a model is. A model is confused when a predicted class does not match the actual class. When they do match, this is considered a true positive (TP). In general, a model resulting in more true positives (TPs) / true negatives (TNs) with fewer false positives (FPs) / false negatives (FNs) is better.
Confusion matrices are used in classification workflows with only one class or with multiple classes, which extends to object detection workflows, too. They help evaluate models by counting classification errors and visualizing class imbalances.

API Reference:
ConfusionMatrix
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Implementation Details#
The implementation of a confusion matrix depends on whether the workflow concerns one or more classes.
SingleClass#
Singleclass confusion matrices are used for binary classification problems. After computing the number of TPs, FPs, FNs, and TNs, a confusion matrix would look like this:
Predicted Positive  Predicted Negative  

Actual Positive  True Positive (TP)  False Negative (FN) 
Actual Negative  False Positive (FP)  True Negative (TN) 
Example: SingleClass#
Let's consider a simple binary classification example and plot a confusion matrix. The table below shows five samples' (three positive and two negative) ground truth labels and inference labels.
Sample 1  Sample 2  Sample 3  Sample 4  Sample 5  

Ground Truth  Cat 
Cat 
Cat 
No Cat 
No Cat 
Inference  Cat 
No Cat 
No Cat 
No Cat 
Cat 
A confusion matrix for this example can be plotted:
Predicted Cat 
Predicted No Cat 


Cat 
1  2 
No Cat 
1  1 
Multiclass#
Multiclass confusion matrices, used for multiclass classification problems, outline counts of TPs, FPs, FNs, and TNs for every unique pair of actual and predicted labels. A multiclass classification confusion matrix with three classes would have the following format:
Predicted Airplane 
Predicted Boat 
Predicted Car 


Actual Airplane 
Correct Prediction  Incorrect Prediction  Incorrect Prediction 
Actual Boat 
Incorrect Prediction  Correct Prediction  Incorrect Prediction 
Actual Car 
Incorrect Prediction  Incorrect Prediction  Correct Prediction 
And for example, if we are trying to calculate the counts of TP, FP, FN, and TN for class Boat
:
Predicted Airplane 
Predicted Boat 
Predicted Car 


Actual Airplane 
True Negative  False Positive  True Negative 
Actual Boat 
False Negative  True Positive  False Negative 
Actual Car 
True Negative  False Positive  True Negative 
Example: Multiclass
Let's take a look at a multiclass classification example and plot a confusion matrix. In this example, we have three
classes: Airplane
, Boat
, and Car
. The multiclass classifier outputs the following inferences:
Sample 1  Sample 2  Sample 3  Sample 4  Sample 5  Sample 6  

Ground Truth  Airplane 
Boat 
Car 
Airplane 
Boat 
Boat 
Inference  Airplane 
Boat 
Airplane 
Airplane 
Boat 
Car 
A confusion matrix for this example can be plotted:
Predicted Airplane 
Predicted Boat 
Predicted Car 


Airplane 
2  0  0 
Boat 
0  2  1 
Car 
1  0  0 
In a different case, these counts may be much higher:
Predicted Airplane 
Predicted Boat 
Predicted Car 


Airplane 
200  0  0 
Boat 
100  8,800  600 
Car 
100  0  1,000 
This confusion matrix reveals that a model is very good at identifying the Boat
class: 8,800 of 9,500 Boat
s were correctly
predicted. Of the 700 incorrect Boat
predictions, 600 were predicted as Car
, and 100 were predicted as Airplane
.
This confusion matrix indicates that when a model makes a Airplane
inference, the model is correct half the time. If
it is incorrect, it has labeled the Airplane
as a Boat
or a Car
.
Whenever there is an actual Airplane
class, the model never predicts that there is a different transportation object.
Normalization#
Sometimes it is easier to focus on classlevel behavior if you are using a normalized confusion matrix. If confusion matrices are colorcoded, normalizing can also create a better visual representation:
You can normalize a confusion matrix by row
(actual classes), column
(predicted classes), or all
(entire matrix).
Each type of normalization surfaces a view sharing different information, which is outlined below.
Normalizing by row
For an actual class, this normalization allows us to see the proportion of correctly or incorrectly predicted
objects for each predicted class. Notice that the diagonal values from this normalization match the
recall per class. To normalize by row
, divide each entry in that row
by the sum of values within
it. If we normalize the multiclass example by row
, we get:
Predicted Airplane 
Predicted Boat 
Predicted Car 


Airplane 
1  0  0 
Boat 
0.01  0.93  0.06 
Car 
0.09  0  0.91 
Normalizing by column
For a predicted class, this normalization allows us to see the proportion of instances predicted as a certain class
that actually belong to each true class. Notice that the diagonal values from this normalization match the
precision per class. To normalize by column
, divide each entry in a column
by the sum of
values within that column
. If we normalize the multiclass example by column
, we get:
Predicted Airplane 
Predicted Boat 
Predicted Car 


Airplane 
0.5  0  0 
Boat 
0.25  1  0.375 
Car 
0.25  0  0.625 
Normalizing by all
For each entry, this normalization allows us to see the overall proportion of instances that fall into a combination
of an actual and predicted class. To normalize by all
, divide each entry by the total sum of all the values in
the matrix. If we normalize the multiclass example by all
, we get:
Predicted Airplane 
Predicted Boat 
Predicted Car 


Airplane 
0.02  0  0 
Boat 
0.01  0.81  0.06 
Car 
0.01  0  0.09 
Limitations and Biases#
Confusion matrices are great for the evaluation of models that deal with multiple classes. They are structured tables of numbers, which is its strength and weakness.
 Class imbalance: Confusion matrices can appear biased when dealing with imbalanced numbers of instances per class, leading to skewed numbers. This can be addressed by normalizing the matrix.
 Categorical evaluation: Confusion matrices have categorical outputs and do not surface any details for misclassifications. All misclassifications are treated equally, so there may be cases where classes are similar or hierarchically related, but confusion matrices will not account for these details.