md=statemodels.formula.ols(formula,data).fit() #data用字典 md.summary(); md.predict({‘x1’:a,‘x2’:}) 或者md=statemodels.api.OLS(y,X).fit(); md.params(); md.predict(X) sklearn.linear_model.LinearRegression().fit(x,
代码如下: summary=model.summaryprint(summary) 1. 2. 通过这段代码,我们可以获取线性回归模型的summary信息,包括参数估计、R方值等。 完整代码示例 下面是完整的代码示例: fromsklearn.datasetsimportload_bostonfromsklearn.linear_modelimportLinearRegression boston=load_boston()X=boston.data y=boston.target mod...
linear_model import LinearRegression Xs = data[['weight', 'length', 'rep78']] y = data['price'].values.reshape(-1, 1) reg = LinearRegression() reg.fit(Xs, y) print("The linear model is: price = {:.5} + {:.5}*weight + {:.5}*length + {:.5}*mpg".format( reg....
from sklearn import linear_model 使用skLearn 进行线性回归建模: X = boston_df[['CRIM', 'ZN', 'INDUS', 'NOX', 'RM', 'AGE', 'DIS', 'TAX','PTRATIO', 'B', 'LSTAT']] y = boston_df['PRICE'] lm = linear_model.LinearRegression() model = lm.fit(X,y) model lm.fit() 函数用...
X)result=logit_model.fit()# 输出模型摘要print(result.summary())(2) sklearn-逻辑回归 # 导入库...
sns.mpl.rcParams['figure.figsize'] = (15.0,9.0)#在这里定义了一个名为linearity_test的函数用来检验线性假设,如果做了不止一线性回归,只要运行这部分代码一次,后面直接可以调用,不必重复写代码deflinearity_test(model, y):''' Function for visually inspecting the assumption of linearity in a linear regress...
print(model.summary()) # 输出回归结果 从回归的拟合结果来看,PPI的回归系数为0.6,且显著不为零。整体模型的R方为65%,可以满足我们的需求。 OLS Regression Results === Dep. Variable: M0012976 R-squared: 0.656 Model: OLS Adj. R-squared: 0.651 Method: Least Squares F-statistic:...
'CuO','PbO','BaO','P2O5','SrO','SnO2','SO2']]Y = df['SiO2']model = LinearRegression()model.fit(X, Y)print("预测结果---",)print(model.coef_)print(model.intercept_)import statsmodels.api as smX2 = sm.add_constant(X)est = sm.OLS(Y,X2).fit()print(est.summary())...
linear_model as sm import pandas as pd ''' # 测试集 Stock_Market = {'Year': [2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016], 'Month': [12, 11,10,9,8,7,6,5,4,3,2,1,12,11,10,9,8,7,6,5,4,...
from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score import statsmodels.api as sm 读取数据 下载数据集后,将数据集放在项目文件的数据目录中,读取数据: data = pd.read_csv("data/Advertising.csv") 查看数据时,输入: data.head() 得到结果如下: 可以看到,“未命名:0”...