擅长:python、mysql、java
<p>据我所知,<code>feature_importances_</code>在XGBoost中没有实现。您可以使用排列功能重要性之类的方法进行自己的滚动:</p>
<pre><code>import random
from sklearn.cross_validation import cross_val_score
def feature_importances(clf, X, y):
score = np.mean(cross_val_score(clf, X,y,scoring='roc_auc'))
importances = {}
for i in range(X.shape[1]):
X_perm = X.copy()
X_perm[:,i] = random.sample(X[:,i].tolist(), X.shape[0])
perm_score = np.mean(cross_val_score(clf, X_perm , y, scoring='roc_auc'))
importances[i] = score - perm_score
return importances
</code></pre>