我是编程新手,我正在学习 Python 以熟悉数据分析和机器学习。
我正在学习有关多元线性回归的向后消除的教程。这是现在的代码:
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('50_Startups.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 4].values
#Taking care of missin' data
#np.set_printoptions(threshold=100)
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer = imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])
#Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelEncoder_X = LabelEncoder()
X[:, 3] = labelEncoder_X.fit_transform(X[:, 3])
onehotecnoder = OneHotEncoder(categorical_features = [3])
X = onehotecnoder.fit_transform(X).toarray()
#Avoid the Dummy Variables Trap
X = X[:, 1:]
#Splitting data in train and test
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
#Fitting multiple Linear Regression to Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
#Predict Test set
regressor = regressor.predict(X_test)
#Building the optimal model using Backward Elimination
import statsmodels.formula.api as sm
a = 0
b = 0
a, b = X.shape
X = np.append(arr = np.ones((a, 1)).astype(int), values = X, axis = 1)
print (X.shape)
X_optimal = X[:,[0,1,2,3,4,5]]
regressor_OLS = sm.OLS(endog = y, exog = X_optimal).fit()
regressor_OLS.summary()
X_optimal = X[:,[0,1,3,4,5]]
regressor_OLS = sm.OLS(endog = y, exog = X_optimal).fit()
regressor_OLS.summary()
X_optimal = X[:,[0,3,4,5]]
regressor_OLS = sm.OLS(endog = y, exog = X_optimal).fit()
regressor_OLS.summary()
X_optimal = X[:,[0,3,5]]
regressor_OLS = sm.OLS(endog = y, exog = X_optimal).fit()
regressor_OLS.summary()
X_optimal = X[:,[0,3]]
regressor_OLS = sm.OLS(endog = y, exog = X_optimal).fit()
regressor_OLS.summary()
现在,执行消除的方式对我来说似乎真的是手动的,我想将其自动化。为了这样做,我想知道是否有办法让我以某种方式返回回归量的 pvalue(例如,如果有一种方法可以在 statsmodels 中执行此操作)。通过这种方式,我认为我应该能够循环 X_optimal 数组的特征,并查看 pvalue 是否大于我的 SL 并将其消除。
谢谢!
最佳答案
遇到了同样的问题。
您可以通过
访问 p 值regressor_OLS.pvalues
它们以科学计数法存储为 float64 数组。我对 python 有点陌生,我确信有更干净、更优雅的解决方案,但这是我的:
sigLevel = 0.05
X_opt = X[:,[0,1,2,3,4,5]]
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()
pVals = regressor_OLS.pvalues
while np.argmax(pVals) > sigLevel:
droppedDimIndex = np.argmax(regressor_OLS.pvalues)
keptDims = list(range(len(X_opt[0])))
keptDims.pop(droppedDimIndex)
print("pval of dim removed: " + str(np.argmax(pVals)))
X_opt = X_opt[:,keptDims]
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
pVals = regressor_OLS.pvalues
print(str(len(pVals)-1) + " dimensions remaining...")
print(pVals)
regressor_OLS.summary()
关于python - [统计模型] : How can I get statsmodel to return the pvalue of an OLS object?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/45740920/