我正在尝试在包含缺失值的数据集上执行RandomForest.
我的数据集如下:
train_data = [['1' 'NaN' 'NaN' '0.0127034' '0.0435092']
['1' 'NaN' 'NaN' '0.0113187' '0.228205']
['1' '0.648' '0.248' '0.0142176' '0.202707']
...,
['1' '0.357' '0.470' '0.0328121' '0.255039']
['1' 'NaN' 'NaN' '0.00311825' '0.0381745']
['1' 'NaN' 'NaN' '0.0332604' '0.2857']]
为了估算"NaN"值,我正在使用:
from sklearn.preprocessing import Imputer
imp=Imputer(missing_values='NaN',strategy='mean',axis=0)
imp.fit(train_data[0::,1::])
new_train_data=imp.transform(train_data)
但是我收到以下错误:
Traceback (most recent call last):
File "./RandomForest.py", line 72, in <module>
new_train_data=imp.transform(train_data)
File "/home/aurore/.local/lib/python2.7/site-packages/sklearn/preprocessing /imputation.py", line 388, in transform
values = np.repeat(valid_statistics, n_missing)
File "/usr/lib/python2.7/dist-packages/numpy/core/fromnumeric.py", line 343, in repeat
return repeat(repeats, axis)
ValueError: a.shape[axis] != len(repeats) …