>>>importwarnings>>>fromsklearn.datasetsimportload_boston>>>withwarnings.catch_warnings():...# You should probably not use this dataset...warnings.filterwarnings("ignore")...X, y =load_boston(return_X_y=True)>>>print(X.shape) (506,13)...
from sklearn.datasets import load_boston boston = load_boston() #导入数据集 print("特征名:",boston.feature_names)#获取特征的名字 print("特征为:",boston.data[0])#获取第一条数据 print("标签为:",boston.target[0])#获取第一条数据的标签 特征名: ['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'R...
使用sklearn.datasets.load_boston即可加载相关数据集 注:load_boston has been removed from scikit-learn since version 1.2. 重要参数: return_X_y: 表示是否返回target( 即价格),默认为False,只返回data( 即属性) from sklearn.datasets import load_boston boston = load_boston() print(boston.data.shape...
from sklearn.datasetsimportload_boston from sklearnimportlinear_model boston=load_boston()data=boston.data target=boston.targetprint(data.shape)print(target.shape)print('系数矩阵:\n',linear_model.LinearRegression().fit(data,target).coef_) iris花卉数据,分类使用。样本数据集的特征默认是一个(150, 4...
导入from sklearn.datasets import load_boston 报错内容: `load_boston` has been removed from scikit-learn since version 1.2.解决办法: 1、将数据下载到本地使用 2、降低 scikit-learn版本:pip install sc…
from sklearn.datasets import load_boston from sklearn.neighbors import KNeighborsRegressor boston = load_boston() boston #回归任务我们使用波士顿房价数据集 1. 2. 3. 4. 波士顿房价数据集如下: #将数据的特征与标签分别赋值给X,y X, y = boston.data, boston.target ...
from sklearn.datasets import load_boston boston = load_boston() print(boston.data.shape) 1. 2. 3. 4. 5. 6. 这个数据集的shape为: (506, 13) (506, 13) 1. 2. 也就是506行,13列,这里13列就是影响房价的13个属性,具体是哪些属性可以通过如下代码打印出来: ...
from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression # 加载波士顿房屋数据集 boston = load_boston() # 数据准备 X = boston.data y = boston.target
load_linnerud([return_X_y]) 加载linnerud 数据集;用于多元回归问题 波士顿房价数据,回归使用。样本数据集的特征默认是一个(506, 13)大小的矩阵,样本值是一个包含506个数值的向量。 #房价数据fromsklearn.datasetsimportload_bostonfromsklearnimportlinear_model ...
from sklearn.datasets import load_boston import pandas as pd import numpy as np import matplotlib import matplotlib.pyplot as plt import seaborn as sns import statsmodels.api as sm %matplotlib inline from sklearn.model_selection import train_test_split ...