我是 python 新手,并试图计算一个简单的线性回归。我的模型有一个因变量和一个自变量。我正在使用 sklearn 包中的 linear_model.LinearRegression() 。我得到了 0.16 的 R 平方值然后我使用 import statsmodels.api as sm mod = sm.OLS(Y_train,X_train) 得到了 0.61 的 R 平方值。下面是从大查询中获取数据的代码
****Code for linear regression****
train_data_df = pd.read_gbq(query,project_id)
train_data_df.head()
X_train = train_data_df.revisit_next_day_rate[:, np.newaxis]
Y_train = train_data_df.demand_1yr_per_new_member[:, np.newaxis]
#scikit-learn version to get prediction R2
model_sci = linear_model.LinearRegression()
model_sci.fit(X_train, Y_train)
print model_sci.intercept_
print ('Coefficients: \n', model_sci.coef_)
print("Residual sum of squares %.2f"
% np.mean((model_sci.predict(X_train) - Y_train ** 2)))
print ('Variance score: %.2f' %model_sci.score(X_train, Y_train))
Y_train_predict = model_sci.predict(X_train)
print ('R Square', r2_score(Y_train,Y_train_predict) )
****for OLM****
print Y_train[:3]
print X_train[:3]
mod = sm.OLS(Y_train,X_train)
res = mod.fit()
print res.summary()
我对此很陌生。试图了解我应该使用哪个线性回归包?