import numpy as np from sklearn import datasets, linear_model#载入数据集“datasets” diabetes = datasets.load_diabetes()#获取糖尿病的数据集 diabetes_X = diabetes.data[:, np.newaxis, 2]#使用其中的一个特征,np.newaxis的作用是增加维度
1、导包, #导包importnumpy as npimportmatplotlib.pyplot as plt%matplotlib inlinefromsklearn.linear_modelimportLinearRegressionimportsklearn.datasets as datasets 2、加载数据集, 糖尿病数据 #获取数据集 diabetesdata =datasets.load_diabetes() data View Code 3、将数据分为 训练数据 和 测试数据 #导包, ...
from sklearn import datasets boston = datasets.load_boston() # 载入boston房价模型 print(dir(boston),"\n",boston.data.shape,"\n",boston.target.shape) #查看模型描述, 特征值数量, 目标数量 from sklearn import linear_model linereg01= linear_model.LinearRegression() #生成一个线性回归实例 # 分...
3. 使用sklearn进行线性回归分析的实例下面将使用sklearn库中的diabetes数据集进行线性回归分析的实例。首先，我们将导入必要的库和数据集。from sklearn import datasetsimport pandas as pdimport numpy as npimport matplotlib.pyplot as plt# 导入diabetes数据集diabetes = datasets.load_diabetes()# 将数据集转换为...
from sklearn.linear_model import LinearRegression #获取糖尿病数据 import sklearn.datasets as datasets diabetes = datasets.load_diabetes() diabetes data = diabetes.data target = diabetes.target feature_names = diabetes.feature_names samples = DataFrame(data, columns = feature_names) ...
from sklearn import datasets class LinearRegression(): def __init__(self):#新建变量 self.w = None def fit(self, X, y):#训练集的拟合 X = np.insert(X, 0, 1, axis=1)#增加一个维度 print (X.shape) X_ = np.linalg.inv(X.T.dot(X))#公式求解 ...
import numpy as np from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LinearRegression # 获取波士顿房价数据,490个样本,每个样本13个特征 boston = datasets.load_boston() X = boston.data Y...
OBJECTIVE The overall goal of this research is to evaluate the usefulness of a symmetric regression line to test agreement on predicted-observed datasets. The specific aims of this study are to: i) discuss the selection of a regression model to fit a line to the predicted-observed scatter, ...
from sklearn.linear_model import LinearRegression import statsmodels.api as sm from scipy import stats diabetes = datasets.load_diabetes() X = diabetes.data y = diabetes.target X2 = sm.add_constant(X) est = sm.OLS(y, X2) est2 = est.fit() ...
from sklearn import datasets boston = datasets.load_boston() # 载入boston房价模型 print(dir(boston),"\n",boston.data.shape,"\n",boston.target.shape) #查看模型描述, 特征值数量, 目标数量 from sklearn import linear_model linereg01= linear_model.LinearRegression() #生成一个线性回归实例 ...