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Computing and Visualizing a Confusion Matrix using Scikit-learn and Pandas in Python

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by vigneshchennai74 dot icon Updated: Feb 27, 2023

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In machine learning, classification models predict the class labels of unseen instances based on the available training data. The performance of these models needs to be evaluated to understand how well they perform on the data. A confusion matrix is one of the most common ways to evaluate classification model performance. 


The confusion matrix is a table that outlines the classification results produced by a model. It contains information about the number of true positives, negatives, false positives, and negatives predicted by the model. These metrics are essential for evaluating model performance, and they can be used to calculate various other metrics such as precision, recall, and F1-score.  


This code demonstrates how to compute and visualize a confusion matrix in Python using the sci-kit-learn and pandas libraries. The confusion_matrix function from sci-kit-learn calculates the confusion matrix for a set of true and predicted labels. The resulting matrix is then converted into a pandas DataFrame, which provides a more readable and interpretable representation of the matrix. 


This code helps evaluate the performance of classification models by providing a quantitative measure of their performance on the given dataset. One can identify which classes are correctly classified and which are misclassified using a confusion matrix. We can use this information to adjust the model's parameters and improve performance. Additionally, the visualization of the confusion matrix can help communicate the model's performance to stakeholders clearly and concisely. 


Here is an example of how to Computing and Visualizing a Confusion Matrix using Scikit-learn and Pandas in Python: 

Preview of the output that you will get on running this code from your IDE

Code

In this solution we have used Scikit-Learn library.

Sci-kit learn how to print labels for confusion matrix?

PythondotLines of Code : 37dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
from sklearn.metrics import confusion_matrix

y_true = ['yes','yes','yes','no','no','no']
y_pred = ['yes','no','no','no','no','no']
print(confusion_matrix(y_true, y_pred))
# Output:
# [[3 0]
#  [2 1]]
print(confusion_matrix(y_true, y_pred, labels=['yes', 'no']))
# Output:
# [[1 2]
#  [0 3]]

import pandas as pd
cmtx = pd.DataFrame(
    confusion_matrix(y_true, y_pred, labels=['yes', 'no']), 
    index=['true:yes', 'true:no'], 
    columns=['pred:yes', 'pred:no']
)
print(cmtx)
# Output:
#           pred:yes  pred:no
# true:yes         1        2
# true:no          0        3

unique_label = np.unique([y_true, y_pred])
cmtx = pd.DataFrame(
    confusion_matrix(y_true, y_pred, labels=unique_label), 
    index=['true:{:}'.format(x) for x in unique_label], 
    columns=['pred:{:}'.format(x) for x in unique_label]
)
print(cmtx)
# Output:
#           pred:no  pred:yes
# true:no         3         0
# true:yes        2         1
  1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
  2. Run the file to create a simple confusion matrix


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for "Sci-kit learn how to print labels for confusion matrix" in kandi. You can try any such use case!


Note


If you want to print the confusion matrix with labels, you may try pandas and set the index and columns of the Dataframe by copying the next set of the code in our snippet.

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created and tested on Vscode 1.75.1 version
  2. The solution is created in Python 3.7.15 version
  3. The solution is tested on the sci-kit-learn 1.0.2 version


Using this solution, we are able to create a simple confusion matrix using Scikit learn library in Python with simple steps. This process also facilities an easy-to-use, hassle-free method to create a hands-on working version of code which would help us get a confusion matrix in Python.

Dependent Library

scikit-learnby scikit-learn

Python doticonstar image 54584 doticonVersion:1.2.2doticon
License: Permissive (BSD-3-Clause)

scikit-learn: machine learning in Python

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            scikit-learnby scikit-learn

            Python doticon star image 54584 doticonVersion:1.2.2doticon License: Permissive (BSD-3-Clause)

            scikit-learn: machine learning in Python
            Support
              Quality
                Security
                  License
                    Reuse

                      If you do not have Scikit-learn that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the Scikit-learn page in kandi.

                      You can search for any dependent library on kandi like Scikit-learn.

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