Regression : Advantages and limitation of Linear Regression

Regression analysis is a key concept in machine learning. The system is learned using both input features and output labels, which is supervised learning. By calculating the impact of each variable on the others, it aids in creating a link between the variables.

Machine learning regression methods are a key idea with several applications.

With the aid of machine learning regression techniques, future values are forecasted. Regression is used to forecast a wide range of future values from the input data/historical data. Regression aids in defining the relationship between label and data points. Label is specified in ML as the target variable (to be predicted).

A supervised learning method in machine learning called regression aids in mapping a prediction relationship between labels and data points.

Linear Regression

Regression analysis's most fundamental type is linear regression. It presupposes that the dependent variable and the predictor have a linear relationship (s).

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Advantages

Utilizing the linear regression approach is quite simple. If the correlation between the independent and dependent variables is known, the regression approach can be used appropriately with ease (Linear Regression for linear relationship).

The significant level for each trait influencing the prediction of the dependent variable is provided via linear regression. We can select the variables that are highly contributing or significant based on this data.

We obtain the best fit line for prediction after completing linear regression, which we can then employ in accordance with the needs of the business.

Limitations with Linear Regression

The primary drawback of linear regression is that it performs poorly when a connection is not linear. Outliers in the dataset can have an impact on linear regression.

Conclusion

In this blog, we learned about Regression in details , advantages and limitation of Linear Regression.