我已经预处理了数据集，并检查了自变量的可能的多重共线性 .
数据集有6列31行，我用它生成1/3作为X_test和y_test，剩下的是X_train和y_train .
我使用sklearn.linear_model LinearRegression函数将X_train和y_train拟合到回归量，并使用X_test的预测函数给出了y的预测值 .
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('daily_raw_status.csv')
X = dataset.iloc[:, :-1].values # IVs
y = dataset.iloc[:, 6].values # DV
# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)
# Fitting MLR to the Training Set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression() # create object
regressor.fit(X_train, y_train) # using fit method, fit the multiple regressor to training set
# Predicting the Test set results
y_pred = regressor.predict(X_test)
Now that I have the y_pred, I can now check the y_pred to the y_test if it's nearly the same.
问题是：
我还能用y_pred做什么，或者我应该把重点放在解释模型上？以及我如何能够将模型重新用于可能的实时数据集的任何想法/概念？
谢谢！
1 回答
此外，你可以这样做：
interpret the beta coefficients and intersect
Find the RMSE or MAE to check the error
如果RMSE或MAE高：异常值处理或feature selection (find potential predictors)