python常用的函数/库的知识 + 论文用到底知识-CFANZ编程社区

Python numpy函数：zeros（）、ones（）、empty（）https://blog.csdn.net/qq_28618765/article/details/78085457

Python的Matplotlib简易教程（速查详细版）——非常全，目录清晰https://blog.csdn.net/qq_35456045/article/details/104528039?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522164862332216782184616980%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=164862332216782184616980&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~top_positive~default-2-104528039.142%5Ev5%5Epc_search_insert_es_download,143%5Ev6%5Eregister&utm_term=matplotlib&spm=1018.2226.3001.4187

我认为，对于回归的预测数值型数据的模型，也可以这么干！！！

大佬的机器学习博客（直接教你怎么用）：

大佬的python机器学习博客

[机器学习]回归--Support Vector Regression(SVR)

机器学习之支持向量回归(SVR)

【Python小程序】第2讲：如何将TXT文件转换成CSV文件？https://blog.csdn.net/wong2016/article/details/97629605?utm_medium=distribute.pc_relevant.none-task-blog-2~default~baidujs_utm_term~default-8.pc_relevant_default&spm=1001.2101.3001.4242.5&utm_relevant_index=11

pd.read_csv用法https://blog.csdn.net/weixin_44056331/article/details/89366105

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import csv
with open('Position_salaries.csv', 'w+',newline='') as csvfile:
    spamwriter = csv.writer(csvfile, dialect='excel')
   
    with open('filename.txt', 'r',encoding='utf-8') as filein:
        for line in filein:
            line_list = line.strip('\n').split('\t')
            spamwriter.writerow(line_list)
txt = pd.read_csv('Position_salaries.csv') 
txtDF = pd.DataFrame(txt)
print(txtDF)

Pandas中loc和iloc函数用法详解https://blog.csdn.net/qq_33217634/article/details/88423660?spm=1001.2101.3001.6650.1&utm_medium=distribute.pc_relevant.none-task-blog-2~default~CTRLIST~Rate-1.pc_relevant_paycolumn_v3&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2~default~CTRLIST~Rate-1.pc_relevant_paycolumn_v3&utm_relevant_index=2

什么是欠拟合现象_欠拟合和过拟合是什么？解决方法总结https://blog.csdn.net/weixin_42433737/article/details/111967325?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522164886742316780255253788%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=164886742316780255253788&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v1~rank_v31_ecpm-6-111967325.142^v5^pc_search_insert_es_download,157^v4^control&utm_term=%E6%8B%9F%E5%90%88%E6%98%AF%E4%BB%80%E4%B9%88%E6%84%8F%E6%80%9D&spm=1018.2226.3001.4187

这个小知识点非常重要！！！

回归指标评价定义及代码（MSE，RMSE，MAE，MAPE，R2-score）https://blog.csdn.net/holal/article/details/106519553?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522164864996916782092980545%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=164864996916782092980545&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v1~rank_v31_ecpm-2-106519553.142%5Ev5%5Epc_search_insert_es_download,143%5Ev6%5Eregister&utm_term=r2-score%E6%98%AF%E4%BB%80%E4%B9%88&spm=1018.2226.3001.4187

MSE
定义：MSE（均方误差）函数一般用来检测模型的预测值和真实值之间的偏差。MSE是真实值与预测值的差值的平方然后求和平均。通过平方的形式便于求导，所以常被用作线性回归的损失函数。
RMSE
定义：RMSE（均方根误差）在MSE的基础上做平方根衡量观测值与真实值之间的偏差。常用来作为机器学习模型预测结果衡量的标准。
MAE
定义：MAE（Mean Absolute Error）平均绝对误差。是绝对误差的平均值。可以更好地反映预测值误差的实际情况。
MAPE
定义：MAPE（平均绝对百分比误差）MAPE 为0%表示完美模型，MAPE 大于 100 %则表示劣质模型。
R2-score
定义：即决定系数，反映因变量的全部变异能通过回归关系被自变量解释的比例。
代码：

# coding=utf-8
import numpy as np
from sklearn import metrics
 
# MAPE需要自己实现
def mape(y_true, y_pred):
    return np.mean(np.abs((y_pred - y_true) / y_true))
 
y_true = np.array([1.0, 5.0, 4.0, 3.0, 2.0, 5.0, -3.0])
y_pred = np.array([1.0, 4.5, 3.8, 3.2, 3.0, 4.8, -2.2])
 
 
print('MSE:',metrics.mean_squared_error(y_true, y_pred))
 
print('RMSE:',np.sqrt(metrics.mean_squared_error(y_true, y_pred)))
 
print('MAE:',metrics.mean_absolute_error(y_true, y_pred))
 
print('MAPE:',mape(y_true, y_pred))
 
## R2-score
from sklearn.metrics import r2_score
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
print('R2-score:',r2_score(y_true, y_pred))

用SVR模型完成对Boston房价的回归预测https://blog.csdn.net/qq_42582489/article/details/106532245?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522164865024416780271981052%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=164865024416780271981052&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_ecpm_v1~rank_v31_ecpm-1-106532245.142%5Ev5%5Epc_search_insert_es_download,143%5Ev6%5Eregister&utm_term=%E7%94%A8SVR%E6%A8%A1%E5%9E%8B%E5%AE%8C%E6%88%90%E5%AF%B9Boston%E6%88%BF%E4%BB%B7%E7%9A%84%E5%9B%9E%E5%BD%92%E9%A2%84%E6%B5%8B&spm=1018.2226.3001.4187这个测试用例very重要的！！！

（可以用来模仿！！！）

python之读取cdvhttps://blog.csdn.net/weixin_34218579/article/details/94682715?spm=1001.2101.3001.6650.4&utm_medium=distribute.pc_relevant.none-task-blog-2~default~CTRLIST~Rate-4.pc_relevant_paycolumn_v3&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2~default~CTRLIST~Rate-4.pc_relevant_paycolumn_v3&utm_relevant_index=9