如何将混淆矩阵的X轴预测标签（0、1）移至顶部？

I'm using the following Python code to plot a confusion matrix for a Random Forest model:

from sklearn.metrics import accuracy_score
plt.figure(figsize=(6,4))
sns.heatmap(cm_rf, annot=True, fmt="d")
plt.title('Random Forest Tree \nAccuracy:{0:.3%}\n'.format(accuracy_score(test_y, predict_rf_y)))
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()

Is there a way to move the X-axis labels (the 0 and 1 under "Predicted label") to the top of the confusion matrix? I'd appreciate any help with this!

Absolutely! You can adjust the position of the x-axis ticks and labels using matplotlib's built-in functions alongside seaborn's heatmap settings. Here are two straightforward, reliable approaches:

Approach 1: Use tick_params and set_xlabel with position control #

After creating the heatmap, you can explicitly shift the x-axis ticks to the top and reposition the main x-label accordingly:

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

plt.figure(figsize=(6,4))
# Assign the heatmap to an axis object for easy manipulation
ax = sns.heatmap(cm_rf, annot=True, fmt="d")

# Move x-axis ticks and their labels to the top, hide bottom ones
ax.tick_params(axis='x', top=True, bottom=False, labeltop=True, labelbottom=False)
# Set the x-label to sit at the top with proper padding
ax.set_xlabel('Predicted label', labelpad=12, loc='top')
ax.set_ylabel('True label')
ax.set_title('Random Forest Tree \nAccuracy:{0:.3%}\n'.format(accuracy_score(test_y, predict_rf_y)))

plt.show()

Approach 2: Use xaxis.set_label_position #

This method directly tells matplotlib to place the x-axis label at the top, paired with tick adjustments:

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

plt.figure(figsize=(6,4))
ax = sns.heatmap(cm_rf, annot=True, fmt="d")

# Set x-axis label position to top
ax.xaxis.set_label_position('top')
# Toggle tick visibility to show only top ticks/labels
ax.tick_params(axis='x', top=True, bottom=False, labeltop=True, labelbottom=False)

ax.set_xlabel('Predicted label')
ax.set_ylabel('True label')
ax.set_title('Random Forest Tree \nAccuracy:{0:.3%}\n'.format(accuracy_score(test_y, predict_rf_y)))

plt.show()

Both methods will move both the "Predicted label" text and the 0/1 tick labels to the top of your confusion matrix. The core idea is controlling tick visibility with tick_params and adjusting the label's anchor point to sit above the plot instead of below.

内容的提问来源于stack exchange，提问作者user7648646