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Plotting multiple confusion matrix side by side

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by vigneshchennai74 dot icon Updated: Feb 27, 2023

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Displaying the confusion matrices side-by-side in a single figure makes it easier to compare the performance of multiple classification algorithms on the same dataset. It allows you to quickly see how each algorithm performs in correctly classifying each class and where it may be making errors. For example, if one algorithm consistently misclassifies a particular class, it will be immediately apparent when comparing its confusion matrix to the other algorithms. This visualization can help you make informed decisions about which algorithm to use for your specific problem and identify areas for improvement or further analysis. 


A confusion matrix is a commonly used tool in evaluating the performance of a machine learning model. It is a table that shows the actual and predicted class labels for a set of test data, which helps in understanding how well the model performs in terms of its ability to classify the samples correctly. 


Scikit-learn, or sklearn, is a Python library for machine learning that provides many tools for building and evaluating predictive models. It is built on top of NumPy, SciPy, and Matplotlib and is widely used by data scientists, researchers, and developers to create machine learning applications. Sklearn provides a range of classification, regression, clustering, and dimensionality reduction algorithms and tools for preprocessing data, cross-validation, and model selection. Sklearn helps in several ways, including: 


  • Making it easy to preprocess and clean data: Sklearn provides a range of tools for data preprocessing, such as handling missing values, scaling and normalizing features, and transforming data. 
  • Providing a wide range of algorithms: Sklearn provides various algorithms for classification, regression, clustering, and dimensionality reduction, making it easy to experiment with different approaches to a given problem. Sklearn is a powerful and flexible library that can be used for various machine-learning tasks, making it an essential tool for anyone in the field. 


This code helps you to compare the performance of different classification algorithms on the Iris dataset by creating and displaying their confusion matrices side-by-side. By comparing the confusion matrices of different algorithms, you can get an idea of their strengths and weaknesses in correctly predicting the species of an Iris flower. This can help you to choose the best algorithm for your particular task or dataset. The resulting visualization helps to compare the performance of different classifiers in terms of their ability to correctly predict the Iris dataset classes. 

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

Code

In this solution we have used Sklearn library.

Plotting multiple confusion matrix side by side

PythondotLines of Code : 48dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier

classifiers = {
    "Naive Bayes": GaussianNB(),
    "LogisiticRegression": LogisticRegression(),
    "KNearest": KNeighborsClassifier(),
    "Support Vector Classifier": SVC(),
    "DecisionTreeClassifier": DecisionTreeClassifier(),
}


iris = load_iris()
X, y = iris.data, iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y)


f, axes = plt.subplots(1, 5, figsize=(20, 5), sharey='row')

for i, (key, classifier) in enumerate(classifiers.items()):
    y_pred = classifier.fit(X_train, y_train).predict(X_test)
    cf_matrix = confusion_matrix(y_test, y_pred)
    disp = ConfusionMatrixDisplay(cf_matrix,
                                  display_labels=iris.target_names)
    disp.plot(ax=axes[i], xticks_rotation=45)
    disp.ax_.set_title(key)
    disp.im_.colorbar.remove()
    disp.ax_.set_xlabel('')
    if i!=0:
        disp.ax_.set_ylabel('')

f.text(0.4, 0.1, 'Predicted label', ha='left')
plt.subplots_adjust(wspace=0.40, hspace=0.1)


f.colorbar(disp.im_, ax=axes)
plt.show()

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.

                      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 using Vscode version 1.75.1
                      2. The solution is created in Python 3.7.15 version
                      3. The solution is tested in scikit-learn 1.0.2 version


                      Using this solution, we are able to create a plot multiple 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 plot confusion matrix in Python.

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