ML——线性回归的优缺点

线性回归是一种基于监督学习的机器学习算法。它执行回归任务。回归模型是基于自变量的目标预测值。它主要用于找出变量和预测之间的关系。请参阅线性回归以获取完整参考。
让我们讨论一下线性回归的一些优点和缺点。

Advantages	Disadvantages
Linear Regression is simple to implement and easier to interpret the output coefficients.	On the other hand in linear regression technique outliers can have huge effects on the regression and boundaries are linear in this technique.
When you know the relationship between the independent and dependent variable have a linear relationship, this algorithm is the best to use because of it’s less complexity to compared to other algorithms.	Diversely, linear regression assumes a linear relationship between dependent and independent variables. That means it assumes that there is a straight-line relationship between them. It assumes independence between attributes.
Linear Regression is susceptible to over-fitting but it can be avoided using some dimensionality reduction techniques, regularization (L1 and L2) techniques and cross-validation.	But then linear regression also looks at a relationship between the mean of the dependent variables and the independent variables. Just as the mean is not a complete description of a single variable, linear regression is not a complete description of relationships among variables.

概括：
线性回归是分析变量之间关系的好工具，但不推荐用于大多数实际应用，因为它通过假设变量之间的线性关系过度简化了现实世界的问题。