Question

大家好，我正在学习机器学习，一开始代码运行良好，但是第二天，当我再次执行代码时，它开始警告我要注意数据集中丢失的数据，我不知道这是什么问题，但是有谁知道解决方案

源代码：

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

dataset = pd.read_csv('Data.csv')

x = dataset.iloc[:, :-1]

y = dataset.iloc[:, 3]


from sklearn.preprocessing import Imputer

imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)

imputer = imputer.fit(x[:, 1:3])

x[:, 1:3] = imputer.transform(x[:, 1:3])

这里是警告：

DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.

Answer 1

SimpleImputer的工作原理几乎与旧的Imputer相似，只是导入并使用它。不再使用Imputer。

from sklearn.impute import SimpleImputer

https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html

Answer 2

from sklearn.impute import SimpleImputer

imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean', verbose = 0)

imputer = imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])

Answer 3

尝试一下。在新的python版本中，SimpleImputer有效。

from sklearn.impute import SimpleImputer

imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean',verbose=0)

imputer = imputer.fit(X[:, 1:3])

X[:, 1:3] = imputer.transform(X[:, 1:3])

Answer 4

照顾丢失的数据

from sklearn.impute import SimpleImputer

imputer = SimpleImputer(missing_values= np.nan, strategy='mean')

imputer = imputer.fit(X.iloc[:, 1:3])
X = imputer.transform(X.iloc[:, 1:3])

在第3行和第4行中使用.iloc会有所帮助！

Answer 5

Imputer仍然可以使用，只需添加其余参数（详细信息和副本）并在必要时填写它们即可。

from sklearn.preprocessing import Imputer

imputer = Imputer(missing_values="NaN", strategy="mean", axis=0, verbose=0, copy="True")

imputer = imputer.fit(X[:, 1:3])

X[:, 1:3] = imputer.transform(X[:, 1:3]))

Answer 6

from sklearn.impute import SimpleImputer

imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean',verbose=0)

imputer = imputer.fit(X[:, 1:3])

X[:, 1:3] = imputer.transform(X[:, 1:3])

scikit-learn中的弃用警告

6 个答案: