Polynomial regression is a machine learning technique used to predict a target variable. It is a form of linear regression but instead of fitting a single-degree line to the data, it uses multiple degrees to fit the data points. This means that it can identify and represent complex relationships between the data points and the target variable.
Polynomial regression can be used for predicting the future trend of a dataset, forecasting output, and estimating relationships between different variables. For example, it can be used to predict the temperature in a city for the next few days or to estimate the relationship between price and demand.
The main advantage of polynomial regression is that it’s ability to handle complex relationships. In comparison to other techniques such as linear regression, polynomial regression is more accurate in predicting the output of the dataset since it can take into account the higher order terms.
The main disadvantage of polynomial regression is overfitting, where the model is trying to fit the noise in the data rather than the actual data points. This can lead to higher training errors and lower generalizations on the test dataset.
Polynomial regression is best used for problems with non-linear datasets, where other techniques such as linear regression fail to provide good results. It is also beneficial in cases where the higher order terms have an influence on the target variable.
Overall, polynomial regression is a powerful machine learning technique that can help identify complex relations in data sets and produce better results than other techniques.
