How do I calculate permutation importance in machine learning?

What is permutation importance in machine learning?

Permutation importance is a technique to assess the importance of features in a model by measuring the increase in prediction error when the values of a feature are permuted.

How do I calculate it?

To calculate permutation importance, you can follow these steps: 1. Train your model. 2. Measure the baseline performance. 3. Shuffle the values of a feature and measure the performance again. 4. The difference in performance indicates the importance of that feature.

Can you show me an example in Python?

Sure! Here's a simple example using scikit-learn:

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.inspection import permutation_importance

# Train your model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Measure baseline performance
baseline = accuracy_score(y_test, model.predict(X_test))

# Calculate permutation importance
result = permutation_importance(model, X_test, y_test, n_repeats=30)
importance = result.importances_mean