使用scikit-learn执行线性回归时遇类型不兼容错误，求解决

问题背景 #

所用数据集包含R&D Spend、Administration、Marketing Spend、State、Profit字段，示例数据如下：

R&D Spend  Administration  Marketing Spend       State     Profit
0   165349.20       136897.80        471784.10    New York  192261.83
1   162597.70       151377.59        443898.53  California  191792.06
2   153441.51       101145.55        407934.54     Florida  191050.39
3   144372.41       118671.85        383199.62    New York  182901.99
4   142107.34        91391.77        366168.42     Florida  166187.94
5   131876.90        99814.71        362861.36    New York  156991.12
6   134615.46       147198.87        127716.82  California  156122.51
7   130298.13       145530.06        323876.68     Florida  155752.60
8   120542.52       148718.95        311613.29    New York  152211.77
9   123334.88       108679.17        304981.62  California  149759.96
10  101913.08       110594.11        229160.95     Florida  146121.95
11  100671.96        91790.61        249744.55  California  144259.40
12   93863.75       127320.38        249839.44     Florida  141585.52
13   91992.39       135495.07        252664.93  California  134307.35
14  119943.24       156547.42        256512.92     Florida  132602.65

编写的建模代码如下：

#Dataset
dataset=pd.read_csv(r'50_Startups.csv')
X=dataset.iloc[:,:-1]
y=dataset.iloc[:,-1]
#Encoding Categorical Data
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
oHe=OneHotEncoder()
ct=ColumnTransformer(transformers=[('encoder',oHe,[3])],remainder='passthrough')
X = np.array(ct.fit_transform(X), dtype = np.str)
#Splitting into Training and Test sets 
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=1)
#Training the Multiple Linear Regression
from sklearn.linear_model import LinearRegression
regressor=LinearRegression()
regressor.fit(X_train,y_train)

运行时触发错误：

ValueError: dtype='numeric' is not compatible with arrays of bytes/strings.
Convert your data to numeric values explicitly instead.

错误原因 #

代码中强制将ColumnTransformer处理后的特征矩阵转换为字符串类型（dtype = np.str），而线性回归模型要求输入特征必须是数值型数据，因此出现类型不兼容的报错。

解决方法 #

去掉强制转换为字符串的操作，直接保留ColumnTransformer输出的数值型矩阵即可。如果需要转换为numpy数组，无需指定字符串类型。

修改后的核心代码行：

X = ct.fit_transform(X)
# 或者如果需要numpy数组：
# X = np.array(ct.fit_transform(X))

完整修改后的代码：

#Dataset
import pandas as pd
import numpy as np
dataset=pd.read_csv(r'50_Startups.csv')
X=dataset.iloc[:,:-1]
y=dataset.iloc[:,-1]
#Encoding Categorical Data
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
oHe=OneHotEncoder()
ct=ColumnTransformer(transformers=[('encoder',oHe,[3])],remainder='passthrough')
X = ct.fit_transform(X)
#Splitting into Training and Test sets 
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=1)
#Training the Multiple Linear Regression
from sklearn.linear_model import LinearRegression
regressor=LinearRegression()
regressor.fit(X_train,y_train)

内容的提问来源于stack exchange，提问作者Yajulu