1. The key knowledge points in this section are summed up in your own words, can be accompanied by pictures, and explain the importance of the knowledge points
Linear regression, as the name suggests, is a regression problem. Since it is a regression problem, it must belong to supervised learning.
Here is a brief introduction to what is a regression problem. Regression is used to predict the relationship between input variables and output variables, especially when the value of the input variable changes, the value of the output variable changes accordingly. The regression model is just a function that represents the mapping from the input variable to the output variable. The learning of the regression problem is equivalent to function fitting: choose a function curve to fit the known data well and predict the unknown data well.
The definition of linear regression is: the target value is expected to be a linear combination of input variables. Linear models are simple in form and easy to model, but they contain some important basic ideas in machine learning. Linear regression is a statistical analysis method that uses regression analysis in mathematical statistics to determine the interdependent quantitative relationship between two or more variables. It is widely used.
Simply put, it is to choose a linear function to fit the known data well and predict the unknown data.
2. Thinking about what linear regression algorithms can be used for? (Everyone try not to write duplicates)
Processing a large amount of observation data to get a mathematical expression that is more in line with the internal laws of things. In other words, the rule between the data and the data is found, so that the result can be simulated, that is, the result is predicted. The solution is to obtain unknown results from known data. For example: prediction of house prices, judgment of credit evaluation, movie box office estimates, etc.
3. Write a linear regression algorithm independently, the data can be made by yourself, or obtained from the Internet. (Plus points)