如何像 R 一样在 Python scikit 中获得回归摘要？
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How to get a regression summary in Python scikit like R does?
提问by mpg
As an R user, I wanted to also get up to speed on scikit.
作为 R 用户，我还想快速了解 scikit。
Creating a linear regression model(s) is fine, but can't seem to find a reasonable way to get a standard summary of regression output.
创建线性回归模型很好，但似乎无法找到一种合理的方法来获得回归输出的标准摘要。
Code example:
代码示例：
# Linear Regression
import numpy as np
from sklearn import datasets
from sklearn.linear_model import LinearRegression
# Load the diabetes datasets
dataset = datasets.load_diabetes()
# Fit a linear regression model to the data
model = LinearRegression()
model.fit(dataset.data, dataset.target)
print(model)
# Make predictions
expected = dataset.target
predicted = model.predict(dataset.data)
# Summarize the fit of the model
mse = np.mean((predicted-expected)**2)
print model.intercept_, model.coef_, mse,
print(model.score(dataset.data, dataset.target))
Issues:
问题：
- seems like the interceptand coefare built into the model, and I just type
print(second to last line) to see them. - What about all the other standard regression output like R^2, adjusted R^2, p values, etc.If I read the examples correctly, seems like you have to write a function/equation for each of these and then print it.
- So, is there no standard summary output for lin. reg. models?
- Also, in my printed array of outputs of coefficients, there are no variable names associated with each of these? I just get the numeric array.Is there a way to print these where I get an output of the coefficients and the variable they go with?
- 似乎截距和系数内置于模型中，我只需键入
print（倒数第二行）即可查看它们。 - 什么其他所有的标准回归输出像R ^ 2，调整R ^ 2，P值等。如果我正确读取的例子，好像你必须写为每个这些函数/公式，然后打印出来。
- 那么，lin.d 没有标准的摘要输出吗？注册。楷模？
- 另外，在我打印的系数输出数组中，没有与这些变量名称相关联的变量名称？我只是得到数字数组。有没有办法打印这些我得到系数和变量的输出？
My printed output:
我的打印输出：
LinearRegression(copy_X=True, fit_intercept=True, normalize=False)
152.133484163 [ -10.01219782 -239.81908937 519.83978679 324.39042769 -792.18416163
476.74583782 101.04457032 177.06417623 751.27932109 67.62538639] 2859.69039877
0.517749425413
Notes: Started off with Linear, Ridge and Lasso. I have gone through the examples. Below is for the basic OLS.
备注：从 Linear、Ridge 和 Lasso 开始。我已经看过这些例子了。以下是基本OLS。
采纳答案by eickenberg
There exists no R type regression summary report in sklearn. The main reason is that sklearn is used for predictive modelling / machine learning and the evaluation criteria are based on performance on previously unseen data (such as predictive r^2 for regression).
sklearn 中没有 R 类型的回归总结报告。主要原因是 sklearn 用于预测建模/机器学习，评估标准基于对以前未见过的数据的性能（例如用于回归的预测 r^2）。
There does exist a summary function for classification called sklearn.metrics.classification_reportwhich calculates several types of (predictive) scores on a classification model.
确实存在一个称为分类的汇总函数，sklearn.metrics.classification_report它计算分类模型上的几种类型的（预测）分数。
For a more classic statistical approach, take a look at statsmodels.
有关更经典的统计方法，请查看statsmodels。
回答by Vinicius Barcelos
Use model.summary()after predict
model.summary()预测后使用
# Linear Regression
import numpy as np
from sklearn import datasets
from sklearn.linear_model import LinearRegression
# load the diabetes datasets
dataset = datasets.load_diabetes()
# fit a linear regression model to the data
model = LinearRegression()
model.fit(dataset.data, dataset.target)
print(model)
# make predictions
expected = dataset.target
predicted = model.predict(dataset.data)
# >>>>>>>Print out the statistics<<<<<<<<<<<<<
model.summary()
# summarize the fit of the model
mse = np.mean((predicted-expected)**2)
print model.intercept_, model.coef_, mse,
print(model.score(dataset.data, dataset.target))
回答by Akshay Dalal
statsmodels package gives a quiet decent summary
statsmodels 包提供了一个安静的体面总结
from statsmodels.api import OLS
OLS(dataset.target,dataset.data).fit().summary()
回答by Naomi Fridman
I use:
我用：
import sklearn.metrics as metrics
def regression_results(y_true, y_pred):
# Regression metrics
explained_variance=metrics.explained_variance_score(y_true, y_pred)
mean_absolute_error=metrics.mean_absolute_error(y_true, y_pred)
mse=metrics.mean_squared_error(y_true, y_pred)
mean_squared_log_error=metrics.mean_squared_log_error(y_true, y_pred)
median_absolute_error=metrics.median_absolute_error(y_true, y_pred)
r2=metrics.r2_score(y_true, y_pred)
print('explained_variance: ', round(explained_variance,4))
print('mean_squared_log_error: ', round(mean_squared_log_error,4))
print('r2: ', round(r2,4))
print('MAE: ', round(mean_absolute_error,4))
print('MSE: ', round(mse,4))
print('RMSE: ', round(np.sqrt(mse),4))
回答by Sahil Kamboj
You can do using statsmodels
您可以使用 statsmodels
import statsmodels.api as sm
X = sm.add_constant(X.ravel())
results = sm.OLS(y,x).fit()
results.summary()
results.summary() will organize the results into three tabels
results.summary() 将结果组织成三个表格
