cannot import name 'BGDRegressor' from 'sklearn.linear_model'
时间: 2023-06-25 17:01:53 浏览: 56
这个错误可能是因为你在导入`sklearn.linear_model`的时候尝试导入`BGDRegressor`,但是`BGDRegressor`不是`sklearn.linear_model`中的一个模型。
`BGDRegressor`可能是一个自定义的模型,或者是一个来自第三方库的模型。如果它是一个自定义模型,那么你需要确保你已经正确地定义和导入了它。如果它来自第三方库,你需要先安装这个库并确认它已经正确地安装。
另外,你也可以尝试导入其他的线性回归模型,例如`LinearRegression`或`Ridge`,它们都是`sklearn.linear_model`中的标准模型。
相关问题
cannot import name 'RandomizedLogisticRegression' from 'sklearn.linear_model'
As an AI language model, I would suggest checking if you have the necessary version of scikit-learn installed or if there is a typo in the import statement. RandomizedLogisticRegression was removed from scikit-learn in version 0.22, so you may need to use a different feature selection method. Alternatively, you could try using an older version of scikit-learn if RandomizedLogisticRegression is necessary for your code.
from sklearn.metrics import mean_squared_error, r2_scorefrom sklearn.linear_model import LinearRegression
# As an AI language model, I cannot give you the complete code for this task without a specific dataset. However, I can give you an example template:
# Importing the required libraries
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.linear_model import LinearRegression
# Reading the dataset
data = pd.read_csv('dataset.csv')
# Splitting the data into training and testing set
train_data = data.iloc[:80,:]
test_data = data.iloc[80:,:]
# Separating the independent and dependent variables
x_train = train_data.iloc[:,:-1]
y_train = train_data.iloc[:,-1]
x_test = test_data.iloc[:,:-1]
y_test = test_data.iloc[:,-1]
# Creating an instance of the Linear Regression model
model = LinearRegression()
# Fitting the model on the training data
model.fit(x_train, y_train)
# Predicting the dependent variable for the test data
y_pred = model.predict(x_test)
# Evaluating the model using Mean Squared Error and R-squared score
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Mean Squared Error:", mse)
print("R-squared score:", r2)
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