machine-learning - Sklearn LabelEncoder引发TypeError排序

我正在使用来自Kaggle的Titanic数据集学习机器学习。我正在使用sklearn的LabelEncoder将文本数据转换为数字标签。以下代码对“性”适用，但对“禁运”无效。

encoder = preprocessing.LabelEncoder()
features["Sex"] = encoder.fit_transform(features["Sex"])
features["Embarked"] = encoder.fit_transform(features["Embarked"])

这是我得到的错误

Traceback (most recent call last):
  File "../src/script.py", line 20, in <module>
    features["Embarked"] = encoder.fit_transform(features["Embarked"])
  File "/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/label.py", line 131, in fit_transform
    self.classes_, y = np.unique(y, return_inverse=True)
  File "/opt/conda/lib/python3.6/site-packages/numpy/lib/arraysetops.py", line 211, in unique
    perm = ar.argsort(kind='mergesort' if return_index else 'quicksort')
TypeError: '>' not supported between instances of 'str' and 'float'

machine-learning - Sklearn LabelEncoder引发TypeError排序-LMLPHP

最佳答案

我自己解决了。问题在于特定功能具有NaN值。用数值替换它仍然会引发错误，因为它具有不同的数据类型。所以我用一个字符值替换了它

 features["Embarked"] = encoder.fit_transform(features["Embarked"].fillna('0'))

关于machine-learning - Sklearn LabelEncoder引发TypeError排序，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/43956705/