Understanding Polynomial Regression in Machine Learning: Everything You Need to Know
Machine Learning is an applied mathematics domain that includes various algorithms that allow computers to learn and generalize patterns from data sets. Polynomial regression is one such algorithm that finds a relationship between a dependent variable and one or more independent variables.
What is Polynomial Regression?
Polynomial Regression is a form of regression analysis that tries to find a polynomial equation that best fits the data points. It is a popular method because it can capture non-linear dependencies between the dependent and independent variables.
The polynomial equation can be represented as:
Y = b0 + b1X + b2X2 + … + bnXn
where Y is the dependent variable and X is the independent variable(s). The coefficients b0, b1, b2, …, bn are the parameters that are estimated from the data.
Why Use Polynomial Regression?
Polynomial regression is useful when the data has a non-linear relationship between the dependent and independent variables. It can capture the curvature of the data and provide a better fit than linear regression. It can also be used to model the interactions between multiple independent variables.
How is Polynomial Regression Applied in Machine Learning?
Polynomial regression is used in various applications in Machine Learning, such as:
1. Predicting housing prices based on the area and other features of a house.
2. Predicting the performance of a machine based on its input parameters.
3. Forecasting the sales of a product based on historical data.
In these applications, polynomial regression is used to find the best-fit curve that can predict the target variable based on the input features.
Key Takeaways
Polynomial Regression is a powerful algorithm that can capture non-linear relationships between the dependent and independent variables. It is useful in many applications in Machine Learning and can provide better predictions than linear regression. Polynomial regression can be used to model the interactions between multiple independent variables and capture the curvature of the data.
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