技术求助：绘制决策边界的代码出现卡顿，问题根源何在？

Hey there, let's walk through the most likely reasons your modified scikit-learn decision boundary code is getting stuck, plus actionable fixes to try:

Common Causes & Solutions #

1. High-Dimensional Feature Space #

The original Iris example uses just 2 features, so plotting the boundary is fast. If your histogram data has many features (e.g., upper=21 suggests 21 features per sample), the code might be trying to generate decision boundaries for every pair of features (like the full Iris example does). This creates a huge number of subplots and calculations, grinding execution to a halt.

Fixes:

• Focus on 2 specific features for plotting (modify the code to select just two columns from X, e.g., X[:, [0,1]] instead of the full dataset)
• Use dimensionality reduction like PCA to compress your data to 2 dimensions first, then plot the boundary on the reduced space

2. Large Number of Training Samples #

If your X has thousands or tens of thousands of samples, combined with a small plot_step=0.02, the code generates an enormous grid of points to predict (e.g., a 1000x1000 grid is 1 million points). Predicting each point with a model (especially a deep decision tree) adds up to massive computation time.

Fixes:

• Downsample your training data to a smaller subset (e.g., take 100-500 samples) for plotting purposes
• Increase plot_step to a larger value like 0.1 or 0.2 to reduce the number of grid points needing prediction

3. Overly Complex Model #

If you're using a decision tree without limiting its depth (the default max_depth=None lets it grow until every leaf is pure), the model becomes extremely complex. Predicting on grid points with such a tree is slow, especially with large datasets.

Fixes:

• Add a max_depth parameter to your decision tree (e.g., DecisionTreeClassifier(max_depth=3) or max_depth=5) to simplify the model
• Test with a lighter model like Logistic Regression first to confirm if the issue is model-related

4. Hidden Data Issues #

Your custom dataset might have anomalies that cause the model or plotting code to hang:

• Missing values (NaN) or infinite values in X that the model can't process cleanly
• Non-numeric feature types (e.g., object dtype instead of float) that trigger unexpected behavior

Fixes:

• Run quick checks on your data:

  import numpy as np
  print("Missing values:", np.isnan(X).any())
  print("Infinite values:", np.isinf(X).any())
  print("Data type:", X.dtype)
  

• Clean the data by imputing missing values or removing problematic samples before training

Quick Debugging Step #

To isolate the issue, start with a minimal test case:

1. Use a tiny subset of your data (e.g., 50 samples)
2. Select only 2 features
3. Set max_depth=2 on your decision tree
4. Increase plot_step to 0.1

If this runs smoothly, you can gradually reintroduce your full data/settings to pinpoint exactly what's causing the slowdown.

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