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Definition of Feature Engineering
Feature Engineering (Feature Engineering) Feature Engineering is the process of transforming raw data into features that better express the nature of the problem, so that applying these features to the prediction model can improve the prediction accuracy of the model for unseen data.
Simply put, feature engineering is to discover features that have a significant impact on the dependent variable y, usually called the independent variable x as a feature, and the purpose of feature engineering is to discover important features.
How can raw data be decomposed and aggregated to better express the essence of the problem? This is the purpose of feature engineering. “feature engineering is manually designing what the input x's should be.” “you have to turn your inputs into things the algorithm can understand.” Feature engineering
is the most time-consuming and important step in data mining model development. -
Polynomial Regression Definition
Study a multinomial regression analysis method between a dependent variable and one or more independent variables, called polynomial regression (Polynomial Regression). If there is only one independent variable, it is called univariate polynomial regression; if there are multiple independent variables, it is called multivariate polynomial regression. -
Examples
such as housing price prediction problem,
[x is a new variable created by the two variables x1 and x2, this is the construction feature, which is the content of feature engineering] [The degree of
fitting of the curve may be higher, which is more in line with the requirements of the topic]
Note: If we use a polynomial regression model, feature scaling is very necessary before running the gradient descent algorithm. [That is, normalization processing , because some data values are large, and some values are small]
Reference content:
In-depth understanding of feature engineering - Zhengyang's article - Zhihu https://zhuanlan.zhihu.com/p/111296130
Baidu Encyclopedia
Wu Enda Machine Learning Notes