Visualizing a Confusion Matrix with Seaborn

You can use Seaborn in combination with other libraries like Scikit-Learn to visualize confusion matrices. Here’s a step-by-step guide on how to do this:

Import the Required Libraries: Import Seaborn for visualization and Scikit-Learn for machine learning and classification metrics.

import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix

Compute the Confusion Matrix: Calculate the confusion matrix using your classification results and the true labels. For example:

true_labels = [1, 0, 1, 1, 0, 1, 0, 0, 1, 0]
predicted_labels = [1, 0, 1, 0, 0, 1, 0, 1, 1, 0]

cm = confusion_matrix(true_labels, predicted_labels)

Create a Heatmap with Seaborn: Visualize the confusion matrix as a heatmap using Seaborn’s sns.heatmap function:

sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)

plt.xlabel('Predicted Labels')
plt.ylabel('True Labels')
plt.title('Confusion Matrix')

Show the Plot: Finally, use plt.show() to display your confusion matrix heatmap.