Plotly: How to make an annotated confusion matrix using a heatmap?

Introduction

Confusion matrices are a vital tool for evaluating the performance of classification models. Plotly, a powerful visualization library, offers an excellent way to create interactive and informative confusion matrices using heatmaps. This article will guide you through the process of creating an annotated confusion matrix with Plotly.

Prerequisites

Before we begin, ensure you have the following installed:

• Python 3.x
• Plotly library: pip install plotly

Creating the Confusion Matrix Data

Sample Data

We’ll use a simple example to demonstrate the concept. Imagine a binary classification problem with two classes: “Class A” and “Class B.” Suppose we have a confusion matrix with the following values:

Data Representation in Python

Let’s represent this data in Python using a list of lists:

 confusion_matrix = [[50, 10], [5, 35]] 

Creating the Annotated Heatmap

Import Libraries

 import plotly.figure_factory as ff 

Create the Heatmap

 fig = ff.create_annotated_heatmap( z=confusion_matrix, x=['Predicted Class A', 'Predicted Class B'], y=['Actual Class A', 'Actual Class B'], annotation_text=confusion_matrix, colorscale='Blues', showscale=True ) 

Customize the Plot

• Title:

 fig.update_layout(title='Confusion Matrix') 

• Axis Labels:

 fig.update_xaxes(title_text='Predicted Class') fig.update_yaxes(title_text='Actual Class') 

• Annotation Appearance:

 fig.update_traces( textfont_size=14, textfont_color='white', texttemplate="%{text}" ) 

Display the Plot

 fig.show() 

Output

This code will generate an interactive annotated confusion matrix heatmap displayed in your browser. The heatmap will visualize the counts within the confusion matrix, with annotations representing the specific values for each cell. You can interact with the heatmap by hovering over the cells and viewing the annotated values.

Conclusion

Plotly’s create_annotated_heatmap function provides a straightforward way to create informative and visually appealing confusion matrices. By annotating the cells with the actual values, you can easily interpret the classification performance of your model and identify areas for improvement.