Question

我尝试通过以下方法获得功能排名：

1. Standardscaler
2. RandomForestClassifier
3. Recursive feature selection


from sklearn.feature_selection import RFECV
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_predict, KFold
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.datasets import load_iris

data = load_iris()

X = data.data
Y = data.target

clf = RandomForestClassifier()

estimators = [('standardize' , StandardScaler()),
             ('rfecv', RFECV(estimator=clf, scoring='accuracy'))]

pipeline = Pipeline(estimators)

ranking_features = pipeline.named_steps['rfecv'].ranking_
print (ranking_features)

AttributeError：“ RFECV”对象没有属性“ ranking _”

欢迎任何实现此目的的最佳实践。

Answer 1

我们首先使用rfecev来拟合数据，然后再调用ranking_属性。尝试运行以下代码：

from sklearn.feature_selection import RFECV
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_predict, KFold
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.datasets import load_iris

data = load_iris()

X = data.data
Y = data.target

clf = RandomForestClassifier()

estimators = [('standardize' , StandardScaler()),
             ('rfecv', RFECV(estimator=clf, scoring='accuracy'))]

# create pipeline
pipeline = Pipeline(estimators)

# fit rfecv to data
rfecv_data = pipeline.named_steps['rfecv'].fit(X, Y)

# get the feature ranking
ranking_features = rfecv_data.ranking_
print (ranking_features)

'Output':
[2 3 1 1]

AttributeError：“ RFECV”对象没有属性“ ranking_”

1 个答案: