How to Plot Confusion Matrix in Python with sklearn

To plot a confusion matrix in Python, use sklearn.metrics.plot_confusion_matrix with your trained model and test data. This function creates a visual matrix showing true vs predicted labels, helping evaluate classification performance.

Syntax

The basic syntax to plot a confusion matrix using sklearn is:

• plot_confusion_matrix(estimator, X, y_true, labels=None, cmap=None, normalize=None)
• estimator: Your trained model.
• X: Test features.
• y_true: True labels for test data.
• labels: Optional list of labels to index the matrix.
• cmap: Color map for the plot.
• normalize: Option to normalize values ('true', 'pred', or 'all').

python

from sklearn.metrics import plot_confusion_matrix

plot_confusion_matrix(estimator, X_test, y_test, cmap='Blues', normalize=None)
import matplotlib.pyplot as plt
plt.show()

Example

This example trains a simple logistic regression model on the iris dataset, predicts test labels, and plots the confusion matrix to visualize classification results.

python

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import plot_confusion_matrix
import matplotlib.pyplot as plt

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

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train model
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

# Plot confusion matrix
plot_confusion_matrix(model, X_test, y_test, cmap='Blues', normalize=None)
plt.title('Confusion Matrix')
plt.show()

Output

A window showing a confusion matrix plot with blue color shading representing counts of true vs predicted labels for iris classes.

Common Pitfalls

• Not fitting the model before plotting causes errors.
• Passing training data instead of test data can give misleading results.
• For multiclass problems, forgetting to specify labels can cause label order confusion.
• Using deprecated functions like confusion_matrix alone without plotting requires extra steps.

Always use plot_confusion_matrix with a trained model and test data for correct visualization.

python

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

# Wrong: plotting confusion matrix without model or plot function
cm = confusion_matrix(y_test, model.predict(X_test))
print(cm)

# Right: use plot_confusion_matrix for direct plotting
from sklearn.metrics import plot_confusion_matrix
plot_confusion_matrix(model, X_test, y_test)
plt.show()

Output

Printed confusion matrix array in console for wrong way; graphical confusion matrix plot for right way.

Quick Reference

Parameter	Description	Example
estimator	Trained model object	LogisticRegression() fitted
X	Test features	X_test array
y_true	True labels for test data	y_test array
labels	List of class labels (optional)	['setosa', 'versicolor', 'virginica']
cmap	Color map for plot	'Blues'
normalize	Normalize counts ('true', 'pred', 'all')	'true'

Key Takeaways

Use sklearn's plot_confusion_matrix with a trained model and test data to visualize classification results easily.

Always fit your model before plotting the confusion matrix to avoid errors.

Use the normalize parameter to see proportions instead of raw counts if needed.

Avoid using confusion_matrix alone for plotting; plot_confusion_matrix handles visualization directly.

Specify labels explicitly for clarity in multiclass problems.