Visualizing a Confusion Matrix with Seaborn

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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:

  1. 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
  1. 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)
  1. 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')
  1. Show the Plot: Finally, use plt.show() to display your confusion matrix heatmap.
See also  How to Make a Countplot in Seaborn

This approach allows you to visualize the confusion matrix as a heatmap with Seaborn, making it easier to understand the model’s performance. You can customize the heatmap’s appearance, including the color map, annotations, and labels, to make it more informative and visually appealing.