Question

我想使用RandomizedLogisticRegression为我的数据集选择变量。但问题是，我的数据集中的一个功能是性别。所以它的值是'F'或'M'而不是数值。结果我得到了以下错误：

Traceback (most recent call last):
  File "main.py", line 84, in Customer_Acquisition_Binary_Logistics
    self.randomized_Logistic_regression()
  File "main.py", line 92, in randomized_Logistic_regression randomized_logistic.fit(X,y)
  File "C:\Python27\lib\site-packages\sklearn\linear_model\randomized_l1.py", line 91, in fit
    X = as_float_array(X, copy=False)
  File "C:\Python27\lib\site-packages\sklearn\utils\validation.py", line 112, in as_float_array
    return X.astype(np.float32 if X.dtype == np.int32 else np.float64) ValueError: could not convert string to float: F

如何处理非数字的分类值？谢谢。

Answer 1

您必须将str值编码为数值，为此您可以使用LabelEncoder：

In [33]:

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
print(le.fit(["paris", "paris", "tokyo", "amsterdam"]))

print(list(le.classes_))

print(le.transform(["tokyo", "tokyo", "paris"]) )

print(list(le.inverse_transform([2, 2, 1])))

LabelEncoder()
['amsterdam', 'paris', 'tokyo']
[2 2 1]
['tokyo', 'tokyo', 'paris']

sklearn.linear_model.RandomizedLogisticRegression：处理分类值

1 个答案: