Day 3 - Advantages and disadvantages of Linear regression
By Jerin Lalichan
1. Advantages of Linear Regression
It is Easy:
Implementing linear regression is easy, and it is simpler to understand the output coefficients.Easy to avoid over-fitting problem:
Over-fitting can occur with linear regression, however, it can be prevented by adopting cross-validation, regularisation (L1 and L2) techniques, and some dimensionality reduction Techniques.
2. Disadvantages of Linear Regression
It assumes a Linear relationship:
A linear model is one in which the independent variables are assumed to linearly explain the dependent variable, such as a = bx + c. No exponential, logarithms, powers, etc. are permitted. Even though this is a great simplification, the real world is not linear. In order to achieve a linear representation, using a linear model would either require us to ignore some patterns or force us to use complex transformations.Data must be independent:
In the general case, that is not always true. But most linear model assumes that the variables in the model are not collinear. ie, they are not correlated.The issue with Outliers:
The linear model assumes the points lie in a straight line and are relatively closer to neighbor points. But in the case of outliers, since they are located too far, it'll be big trouble for the model performance.Summary:
Linear Regression is a great technique to analyze the relationships between the variables but it isn’t recommended for most practical applications because it over-simplifies real-world problems by assuming a linear relationship among the variables.
I am doing a challenge - #66DaysofData in which I will be learning something new from the Data Science field for 66 days, and I will be posting daily topics on my LinkedIn, On my GitHub repository, and on my blog as well.
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