博客
关于我
用线性回归计算缺失值
阅读量:352 次
发布时间:2019-03-04

本文共 2672 字,大约阅读时间需要 8 分钟。

  • Missing data

    Missing data can grocely be classified into three types:

    1. MCAR(Missing Completely At Random), which means that there is nothing systematic about why some date is missing. That is, there is no relationship between the fact that data is missing and either the observed or unobserved covariates.
    2. MAR(Missing At Random), resembles MCAR because there still is an element of randomness.
    3. MNAR(Missing Not At Random), implies that the fact that fata is missing is directly correlated with the value of the misssing data.
  • How to deal with missing data

    1. Just delete missing entries
    2. Replaceing missing values with the mean or median
    3. Linear Regression

      First, several predictors of the variable with missing values are identified using a correlation matrix. The best predictors are selected and used as independent variables in a regression equation.

      The variable with missing data is used as the dependent variable.

      Second, cases with complete data for the predictor variables are used to generate the regression equation;

      Third, the equation is then used to predict missing values for incomplete cases in an iterative process.

      以上是单变量线性回归

    4. 多元线性回归

      Linear regression has signigicant limits like:

      • It can’t easily match any data set that is non-linear
      • It can only be used to make predictions that fit within the range of the training data set
      • It can only be fit to data sets with a single dependent variables and a single independent variable

      This is where multiple regression comes in. It is specifically designed to create regressions on models with a single dependent variable and multiple independent variables.

      Equation for multiple regpression takes the form:

      y = b 1 ∗ x 1 + b 2 ∗ x 2 + . . . + b n ∗ x n + a y=b_1*x_1+b_2*x_2+...+b_n*x_n+a y=b1x1+b2x2+...+bnxn+a
      b i b_i bi coefficients;

      x i x_i xi independent variables; also called predictor variables

      y i y_i yi dependent vairables; also called criterion variable

      a a a a constant stating the value of the depnedent variable;

      How to fit a multiple regression model ?

      Similarly to minimized the sum of squared errors to find B in the linear regression, we minimize the sum of squared errors to find all the B terms in multiple regression.

      Exactly we use stochastic gradient descent(随机梯度下降).

      How to make sure the model fits the data well ?

      Use the same r 2 r^2 r2 value that was used for linear regression.

      r 2 r^2 r2 which is called the coefficient of determination, states the portion of change in the data set that is predicted by the model. It’s a value ranging from 0 to 1. With 0 stating that the model has no ability to predict the result and 1 stating that the model predicts the result perfectly.

  • References

转载地址:http://pjge.baihongyu.com/

你可能感兴趣的文章
mysql 字段类型类型
查看>>
MySQL 字符串截取函数,字段截取,字符串截取
查看>>
MySQL 存储引擎
查看>>
mysql 存储过程 注入_mysql 视图 事务 存储过程 SQL注入
查看>>
MySQL 存储过程参数:in、out、inout
查看>>
mysql 存储过程每隔一段时间执行一次
查看>>
mysql 存在update不存在insert
查看>>
Mysql 学习总结(86)—— Mysql 的 JSON 数据类型正确使用姿势
查看>>
Mysql 学习总结(87)—— Mysql 执行计划(Explain)再总结
查看>>
Mysql 学习总结(88)—— Mysql 官方为什么不推荐用雪花 id 和 uuid 做 MySQL 主键
查看>>
Mysql 学习总结(89)—— Mysql 库表容量统计
查看>>
mysql 实现主从复制/主从同步
查看>>
mysql 审核_审核MySQL数据库上的登录
查看>>
mysql 导入 sql 文件时 ERROR 1046 (3D000) no database selected 错误的解决
查看>>
mysql 导入导出大文件
查看>>
mysql 将null转代为0
查看>>
mysql 常用
查看>>
MySQL 常用列类型
查看>>
mysql 常用命令
查看>>
Mysql 常见ALTER TABLE操作
查看>>