我尝试使用scikit-learnOOB ROC作为对递归过程中创建的每个子集进行评分的方法，使用和随机森林分类器来执行递归特征消除。
但是，当我尝试使用该RFECV方法时，出现错误提示AttributeError: 'RandomForestClassifier' object has no attribute 'coef_'
随机森林本身没有系数，但是它们确实具有按基尼分数划分的排名。所以，我想知道如何解决这个问题。
请注意，我要使用一种方法，该方法会明确告诉我从pandasDataFrame的哪些特征中选择了最佳分组，因为我正在使用递归特征选择以尽量减少将输入到最终分类器中的数据量。
这是一些示例代码：
from sklearn import datasets
import pandas as pd
from pandas import Series
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import RFECV
iris = datasets.load_iris()
x=pd.DataFrame(iris.data, columns=['var1','var2','var3', 'var4'])
y=pd.Series(iris.target, name='target')
rf = RandomForestClassifier(n_estimators=500, min_samples_leaf=5, n_jobs=-1)
rfecv = RFECV(estimator=rf, step=1, cv=10, scoring='ROC', verbose=2)
selector=rfecv.fit(x, y)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/bbalin/anaconda/lib/python2.7/site-packages/sklearn/feature_selection/rfe.py", line 336, in fit
ranking_ = rfe.fit(X_train, y_train).ranking_
File "/Users/bbalin/anaconda/lib/python2.7/site-packages/sklearn/feature_selection/rfe.py", line 148, in fit
if estimator.coef_.ndim > 1:
AttributeError: 'RandomForestClassifier' object has no attribute 'coef_'
这是我的想法。这是一个非常简单的解决方案，并且由于我要对高度不平衡的数据集进行分类，因此它依赖于自定义精度指标（称为weightedAccuracy）。但是，如果需要，应该很容易将其扩展。
from sklearn import datasets
import pandas
from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation
from sklearn.metrics import confusion_matrix
def get_enhanced_confusion_matrix(actuals, predictions, labels):
""""enhances confusion_matrix by adding sensivity and specificity metrics"""
cm = confusion_matrix(actuals, predictions, labels = labels)
sensitivity = float(cm[1][1]) / float(cm[1][0]+cm[1][1])
specificity = float(cm[0][0]) / float(cm[0][0]+cm[0][1])
weightedAccuracy = (sensitivity * 0.9) + (specificity * 0.1)
return cm, sensitivity, specificity, weightedAccuracy
iris = datasets.load_iris()
x=pandas.DataFrame(iris.data, columns=['var1','var2','var3', 'var4'])
y=pandas.Series(iris.target, name='target')
response, _ = pandas.factorize(y)
xTrain, xTest, yTrain, yTest = cross_validation.train_test_split(x, response, test_size = .25, random_state = 36583)
print "building the first forest"
rf = RandomForestClassifier(n_estimators = 500, min_samples_split = 2, n_jobs = -1, verbose = 1)
rf.fit(xTrain, yTrain)
importances = pandas.DataFrame({'name':x.columns,'imp':rf.feature_importances_
}).sort(['imp'], ascending = False).reset_index(drop = True)
cm, sensitivity, specificity, weightedAccuracy = get_enhanced_confusion_matrix(yTest, rf.predict(xTest), [0,1])
numFeatures = len(x.columns)
rfeMatrix = pandas.DataFrame({'numFeatures':[numFeatures],
'weightedAccuracy':[weightedAccuracy],
'sensitivity':[sensitivity],
'specificity':[specificity]})
print "running RFE on %d features"%numFeatures
for i in range(1,numFeatures,1):
varsUsed = importances['name'][0:i]
print "now using %d of %s features"%(len(varsUsed), numFeatures)
xTrain, xTest, yTrain, yTest = cross_validation.train_test_split(x[varsUsed], response, test_size = .25)
rf = RandomForestClassifier(n_estimators = 500, min_samples_split = 2,
n_jobs = -1, verbose = 1)
rf.fit(xTrain, yTrain)
cm, sensitivity, specificity, weightedAccuracy = get_enhanced_confusion_matrix(yTest, rf.predict(xTest), [0,1])
print("\n"+str(cm))
print('the sensitivity is %d percent'%(sensitivity * 100))
print('the specificity is %d percent'%(specificity * 100))
print('the weighted accuracy is %d percent'%(weightedAccuracy * 100))
rfeMatrix = rfeMatrix.append(
pandas.DataFrame({'numFeatures':[len(varsUsed)],
'weightedAccuracy':[weightedAccuracy],
'sensitivity':[sensitivity],
'specificity':[specificity]}), ignore_index = True)
print("\n"+str(rfeMatrix))
maxAccuracy = rfeMatrix.weightedAccuracy.max()
maxAccuracyFeatures = min(rfeMatrix.numFeatures[rfeMatrix.weightedAccuracy == maxAccuracy])
featuresUsed = importances['name'][0:maxAccuracyFeatures].tolist()
print "the final features used are %s"%featuresUsed
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