機器學習—迴歸2-4（嶺迴歸）
阿新 • • 發佈：2022-03-14
使用嶺迴歸根據多個因素預測醫療費用
資料集連結：https://www.cnblogs.com/ojbtospark/p/16005626.html
主要流程步驟：
1. 匯入包
In [1]:# 匯入包
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
2. 匯入資料集
In [2]:# 匯入資料集
data = pd.read_csv('insurance.csv')
data.head()
Out[2]:| age | sex | bmi | children | smoker | region | charges | |
|---|---|---|---|---|---|---|---|
| 0 | 19 | female | 27.900 | 0 | yes | southwest | 16884.92400 |
| 1 | 18 | male | 33.770 | 1 | no | southeast | 1725.55230 |
| 2 | 28 | male | 33.000 | 3 | no | southeast | 4449.46200 |
| 3 | 33 | male | 22.705 | 0 | no | northwest | 21984.47061 |
| 4 | 32 | male | 28.880 | 0 | no | northwest | 3866.85520 |
3. 資料預處理
3.1 檢測缺失值
In [3]:
# 檢測缺失值
null_df = data.isnull().sum()
null_df
Out[3]:
age 0
sex 0
bmi 0
children 0
smoker 0
region 0
charges 0
dtype: int64
3.2 標籤編碼&獨熱編碼
In [4]:# 標籤編碼&獨熱編碼
data = pd.get_dummies(data, drop_first = True)
data.head()
Out[4]:
| age | bmi | children | charges | sex_male | smoker_yes | region_northwest | region_southeast | region_southwest | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 19 | 27.900 | 0 | 16884.92400 | 0 | 1 | 0 | 0 | 1 |
| 1 | 18 | 33.770 | 1 | 1725.55230 | 1 | 0 | 0 | 1 | 0 |
| 2 | 28 | 33.000 | 3 | 4449.46200 | 1 | 0 | 0 | 1 | 0 |
| 3 | 33 | 22.705 | 0 | 21984.47061 | 1 | 0 | 1 | 0 | 0 |
| 4 | 32 | 28.880 | 0 | 3866.85520 | 1 | 0 | 1 | 0 | 0 |
3.3 得到自變數和因變數
In [5]:# 得到自變數和因變數
y = data['charges'].values
data = data.drop(['charges'], axis = 1)
x = data.values
3.4 拆分訓練集和測試集
In [6]:# 拆分訓練集和測試集
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 = 1)
print(x_train.shape)
print(x_test.shape)
print(y_train.shape)
print(y_test.shape)
(1070, 8)
(268, 8)
(1070,)
(268,)
4. 構建不同引數的嶺迴歸模型
4.1 模型1：構建嶺迴歸模型
4.1.1 構建嶺迴歸模型
In [7]:# 構建不同引數的嶺迴歸模型
# 模型1：構建嶺迴歸模型（alpha = 20）
from sklearn.linear_model import Ridge
regressor = Ridge(alpha = 20, normalize = True, fit_intercept = True)
regressor.fit(x_train, y_train)
Out[7]:
Ridge(alpha=20, normalize=True)
4.1.2 得到數學表示式
In [8]:# 得到數學表示式
print('數學表示式是：\n Charges = ', end='')
columns = data.columns
coefs = regressor.coef_
for i in range(len(columns)):
print('%s * %.2f + ' %(columns[i], coefs[i]), end='')
print(regressor.intercept_)
數學表示式是：
Charges = age * 12.48 + bmi * 17.21 + children * 14.86 + sex_male * 60.23 + smoker_yes * 1121.22 + region_northwest * -34.52 + region_southeast * 61.62 + region_southwest * -33.53 + 11938.446490743021
4.1.3 預測測試集
In [9]:# 預測測試集
y_pred = regressor.predict(x_test)
4.1.4 得到模型MSE
In [10]:# 得到模型 MSE
from sklearn.metrics import mean_squared_error
mse_score = mean_squared_error(y_test, y_pred)
print('alpha=20時，嶺迴歸模型的MSE是：' , format(mse_score, ','))
alpha=20時，嶺迴歸模型的MSE是： 138,769,173.1285671
4.2 模型2：構建嶺迴歸模型
In [11]:# 模型2：構建嶺迴歸模型（alpha = 0.1）
regressor = Ridge(alpha = 0.1, normalize = True, fit_intercept = True)
regressor.fit(x_train, y_train)
Out[11]:
Ridge(alpha=0.1, normalize=True)
In [12]:
# 得到線性表示式
print('數學表示式是：\n Charges = ', end='')
columns = data.columns
coefs = regressor.coef_
for i in range(len(columns)):
print('%s * %.2f + ' %(columns[i], coefs[i]), end='')
print(regressor.intercept_)
數學表示式是：
Charges = age * 234.53 + bmi * 291.63 + children * 361.72 + sex_male * -88.02 + smoker_yes * 21586.00 + region_northwest * -266.87 + region_southeast * -672.40 + region_southwest * -691.71 + -9237.600606458109
In [13]:
# 預測測試集
y_pred = regressor.predict(x_test)
In [14]:
# 得到模型的MSE
mse_score = mean_squared_error(y_test, y_pred)
print('alpha=0.1時，嶺迴歸模型的MSE是：' , format(mse_score, ','))
alpha=0.1時，嶺迴歸模型的MSE是： 36,841,099.26516503
4.3 模型3：構建嶺迴歸模型
In [15]:# 模型3：構建嶺迴歸模型（alpha = 0.01）
regressor = Ridge(alpha = 0.01, normalize = True, fit_intercept = True)
regressor.fit(x_train, y_train)
Out[15]:
Ridge(alpha=0.01, normalize=True)
In [16]:
# 得到線性表示式
print('數學表示式是：\n Charges = ', end='')
columns = data.columns
coefs = regressor.coef_
for i in range(len(columns)):
print('%s * %.2f + ' %(columns[i], coefs[i]), end='')
print(regressor.intercept_)
數學表示式是：
Charges = age * 255.00 + bmi * 318.27 + children * 402.86 + sex_male * -223.99 + smoker_yes * 23546.28 + region_northwest * -377.66 + region_southeast * -992.59 + region_southwest * -875.29 + -11075.028462288014
In [17]:
# 預測測試集
y_pred = regressor.predict(x_test)
In [18]:
# 得到模型的MSE
mse_score = mean_squared_error(y_test, y_pred)
print('alpha=0.01時，嶺迴歸模型的MSE是：' , format(mse_score, ','))
alpha=0.01時，嶺迴歸模型的MSE是： 35,539,055.332710184
4.4 模型4：構建嶺迴歸模型
In [19]:# 模型4：構建嶺迴歸模型（alpha = 0.0001）
regressor = Ridge(alpha = 0.0001, normalize = True, fit_intercept = True)
regressor.fit(x_train, y_train)
Out[19]:
Ridge(alpha=0.0001, normalize=True)
In [20]:
# 得到線性表示式
print('數學表示式是：\n Charges = ', end='')
columns = data.columns
coefs = regressor.coef_
for i in range(len(columns)):
print('%s * %.2f + ' %(columns[i], coefs[i]), end='')
print(regressor.intercept_)
數學表示式是：
Charges = age * 257.47 + bmi * 321.59 + children * 408.01 + sex_male * -241.97 + smoker_yes * 23784.06 + region_northwest * -395.90 + region_southeast * -1037.90 + region_southwest * -902.75 + -11295.364555495733
In [21]:
# 預測測試集
y_pred = regressor.predict(x_test)
In [22]:
# 得到模型的MSE
mse_score = mean_squared_error(y_test, y_pred)
print('alpha=0.0001時，嶺迴歸模型的MSE是：' , format(mse_score, ','))
alpha=0.0001時，嶺迴歸模型的MSE是： 35,479,846.30114783
結論： 由上面4個模型可見，不同的模型超引數對嶺迴歸模型效能的影響不同。