Confusion Matrix
Chart overview
A confusion matrix displays the counts of correct and incorrect predictions for each class in a classification model, arranged as a square grid.
Key points
- Researchers in machine learning use it to identify which classes a model confuses with one another, revealing precision, recall, and F1-score trade-offs at a glance.
- It is the starting point for diagnosing and improving any supervised classification system.
Python Tutorial
How to create a confusion matrix in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
How to Create a Heatmap in PythonExample Visualization
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"Create a confusion matrix heatmap from my classification results. Use a diverging colormap, annotate each cell with counts and percentages, label axes with class names, and add a colorbar. Normalize rows to show recall per class when requested."
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Python Code Example
Console Output
Figure saved: plotivy-confusion-matrix.png
Common Use Cases
- 1Evaluating multi-class image classification model performance
- 2Diagnosing which disease classes are confused in a medical AI system
- 3Comparing model performance before and after hyperparameter tuning
- 4Reporting per-class accuracy in NLP text classification tasks
Pro Tips
Normalize rows to show recall per class when class sizes are imbalanced
Use a sequential colormap (Blues) for normalized matrices to avoid misleading divergence
Rotate x-axis labels 45 degrees when class names are long to prevent overlap
Add a classification report table below the matrix for precision and F1 scores
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for confusion-matrix.
numpy
Useful in specialized workflows that complement core Python plotting libraries for confusion-matrix analysis tasks.
sklearn
Useful in specialized workflows that complement core Python plotting libraries for confusion-matrix analysis tasks.
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