Python的 - 与scikit错误学习随机森林约值格式

Question

当我执行命令：

clf.fit(train_data, train_label) 

我获得以下错误

ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

的问题是阵列train_data大小为（ 18000,20）。我试图用这个命令：

clf.fit(np.float32(train_data), train_label) 

或

train_data = np.array([s[0].astype('float32') for s in train_data]) 

查找下面的链接火车文件（Python）的数据集train_data和train_label：

https://www.dropbox.com/s/b3017gi18x6x325/train?dl=0

但是，我无法得到数组“train_data”中的所有值对clf.fit函数有效。任何帮助？

Answer 1

只是找到了一个解决方案来克服这个错误。您需要缩放数据：

代码：

from sklearn.ensemble import RandomForestClassifier 
import pickle 
import numpy as np 
from sklearn.preprocessing import scale 

with open('train', 'rb') as f: 
    train_data, train_label = pickle.load(f) 

#some diagnostic to see if there are NaNs. No NaN were found ! 
print(np.isnan(train_data)) 
print(np.where(np.isnan(train_data))) 
print(np.nan_to_num(train_data)) 
print(np.isnan(train_label)) 
print(np.where(np.isnan(train_label))) 

#so need to scale 
train_data = scale(train_data) 

clf = RandomForestClassifier() 
clf.fit(train_data, train_label)