import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split 3.2 定义随机数种子 在机器学习任务中,随机数种子的作用是确保实验结果的可重复性。具体来说,机器学习算
import numpy as np from sklearn import datasets,linear_model path=r'D:\daacheng\Python\PythonCode\machineLearning\Delivery.csv' data=genfromtxt(path,delimiter=',') print(data) x=data[:,:-1] y=data[:,-1] regr=linear_model.LinearRegression()#创建模型 regr.fit(x,y) #y=b0+b1*x1+b2...
multiple linear regression 我使用skleanrn训练了一组数据,其中数据使用pandas库读取excel表,求出测试数据的均方误差和画出测试数据与预测值的图。数据集去我的资源下载Advertising.csv 1.交叉验证的库 from sklearn.model_selection import train_test_split 2.pandas的两个主要数据结构:S...scikit...
Multiple_LinearRegression_Test2 1importcsv2importnumpy as np3fromsklearnimportdatasets,linear_model45with open("car_2.1.csv") as f:6car_data = list(csv.reader(f))#转换为list7data_X = [row[:5]forrowincar_data[:-1]]#变量x8data_Y = [row[-1]forrowincar_data[:-1]]#值y9xPred = ...
For the analysis of data (data from administered questionnaires and interviews), a model was built using python with sklearn in multiple regression, with the coefficient, the intercept, and the adjusted r-square well examined. The research shows a correlation between foreigners living in China and...
用sklearn.linear_model库的LinearRegression类,在数据集上训练模型。首先,创建一个LinearRegression的对象regressor,接着用LinearRegression类的fit()方法,用对象regressor在数据集上进行训练。 regressor = LinearRegression() regressor.fit(X_train, Y_train) ...
from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(x_train, y_train) Step 9: Extract the y-intercept print(regressor.intercept_) Step 10: Extract the regression coefficient print(regressor.coef_)
%matplotlibinlineimportmatplotlib.pyplotaspltfromsklearnimportlinear_modelfromsklearn.model_selectionimporttrain_test_splitimportnumpyasnpimportpandasaspdimportseabornassns Load data data=pd.read_csv('Multiple Linear Regression.csv') View data data.head() ...
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Multiple_LinearRegression_Test 多变量的线性回归问题 1 import csv 2 import numpy as np 3 import pandas as pd 4 from sklearn import datasets,linear_model 5 6 with open("car.csv","r") as f: 7 data = list(csv.reader(f)) 8 data_X = [row[:2] for row in data[1:]] 9 data_Y ...