[数据结构与算法]ML_Multiple Linear Regression

第一步：导包

#Step 1: Data Preprocessing
import pandas as pd
import numpy as np

第二步：导入数据

#Step 2: Importing the dataset
dataset = pd.read_csv('D:/daily/机器学习100天/100-Days-Of-ML-Code-中文版本/100-Days-Of-ML-Code-master/datasets/50_Startups.csv')
X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : ,  4 ].values

第三步：编码

#Step 3: Encoding Categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder = LabelEncoder()
X_3 = labelencoder.fit_transform(X[:,3])
X[:,3] = X_3
print(X_3)
State = X[:,3]
State = State.reshape(-1,1)  # 转换为多行一列的形式
env = OneHotEncoder(categories = 'auto').fit(State)
res = env.transform(State).toarray()
X = np.hstack((X[:, :3], res))  # OneHot后的最后一列与之前两列拼接起来

打印：
X_3

[2 0 1 2 1 2 0 1 2 0 1 0 1 0 1 2 0 2 1 2 0 2 1 1 2 0 1 2 1 2 1 2 0 1 0 2 1
 0 2 0 0 1 0 2 0 2 1 0 2 0]

res

[[0. 0. 1.]
 [1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]
 [0. 1. 0.]
 [0. 0. 1.]
......

即2的独热编码为[0,0,1]，0的独热编码为[1,0,0]，1的独热编码为[0,1,0]。

第四步：避免虚拟变量陷阱

#Step 4: Avoiding Dummy Variable Trap
X = X[ : , :3]

第五步：划分训练集、测试集

#Step 5: Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

第六步：多重线性回归拟合

#Step 6: Fitting Multiple Linear Regression to the Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, Y_train)

第七步：预测

#Step 7: Predicting the Test set results
y_pred = regressor.predict(X_test)

第八步：回归性能指标

#Step 8: regression evaluation
from sklearn.metrics import r2_score
print(Y_test)
print(y_pred)
print(r2_score(Y_test, y_pred))

打印：0.9393955917820571

R2 决定系数（拟合优度），模型越好：r2→1；模型越差：r2→0

完整代码：

#Day 3: Multiple Linear Regression 2022/4/6

#Step 1: Importing the libraries
import pandas as pd
import numpy as np

#Step 2: Importing the dataset
dataset = pd.read_csv('D:/daily/机器学习100天/100-Days-Of-ML-Code-中文版本/100-Days-Of-ML-Code-master/datasets/50_Startups.csv')
X = dataset.iloc[ : , :-1].values
Y = dataset.iloc[ : ,  4 ].values

#Step 3: Encoding Categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder = LabelEncoder()
X_3 = labelencoder.fit_transform(X[:,3])
X[:,3] = X_3
print(X_3)
State = X[:,3]
State = State.reshape(-1,1)  # 转换为多行一列的形式
env = OneHotEncoder(categories = 'auto').fit(State)
res = env.transform(State).toarray()
X = np.hstack((X[:, :3], res))  # OneHot后的最后一列与之前两列拼接起来

#Step 4: Avoiding Dummy Variable Trap
X = X[ : , :3]
print(X)
#Step 5: Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

#Step 6: Fitting Multiple Linear Regression to the Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, Y_train)

#Step 7: Predicting the Test set results
Y_pred = regressor.predict(X_test)

#Step 8: regression evaluation
from sklearn.metrics import r2_score
#print(Y_test)
#print(Y_pred)
print(r2_score(Y_test, Y_pred))