scikit功能重要性选择经验

Question

Scikit-learn has a mechanism to rank features (classification) using extreme randomized trees.

forest = ExtraTreesClassifier(n_estimators=250,
                          compute_importances=True,
                          random_state=0)

I have a question if this method is doing a "univariate" or "multivariate" feature ranking. Univariate case is where individual features are compared to each other. I would appreciate some clarifications here. Any other parameters that I should try to fiddle? Any experiences and pitfalls with this ranking methhod are also appreciated. THe output of this ranking identify feature numbers(5,20,7. I would like to check if the feature number really corresponds to the row in the feature matrix. THat is, the feature number 5 corresponds to the sixth row in the feature matrix (starts with 0).

Answer 1

I'm not an expert but this is not univariate. In fact the total feature importance is computed from the feature importance of each tree (taking the mean value i think).

For each tree, the importances are computed from the impurity of the split .

I used this method and it seems to give good results, better from my point of view than the univariate method. But I don't know any technique to test the results except the knowledge of the dataset.

To order, the feature correctly you should follow this example and modify it a bit like so to use pandas.DataFrame and their proper column names:

import numpy as np

from sklearn.ensemble import ExtraTreesClassifier

X = pandas.DataFrame(...)
Y = pandas.Series(...)

# Build a forest and compute the feature importances
forest = ExtraTreesClassifier(n_estimators=250,
                              random_state=0)

forest.fit(X, y)

feature_importance = forest.feature_importances_
feature_importance = 100.0 * (feature_importance / feature_importance.max())
sorted_idx = np.argsort(feature_importance)[::-1]
print "Feature importance:"
i=1
for f,w in zip(X.columns[sorted_idx], feature_importance[sorted_idx]):
    print "%d) %s : %d" % (i, f, w)
    i+=1
pos = np.arange(sorted_idx.shape[0]) + .5
plt.subplot(1, 2, 2)
nb_to_display = 30
plt.barh(pos[:nb_to_display], feature_importance[sorted_idx][:nb_to_display], align='center')
plt.yticks(pos[:nb_to_display], X.columns[sorted_idx][:nb_to_display])
plt.xlabel('Relative Importance')
plt.title('Variable Importance')
plt.show()