Regression
Regression is an important concept within machine learning. It is a technique used to build a predictive model based on historical data. In the context of machine learning, regression refers to the process of identifying and modeling the relationship between a dependent variable and one or more independent variables. This allows us to predict future values of the dependent variable based on the known values of the independent variables.
Regression in Machine Learning
Regression in machine learning is the process of building a predictive model capable of modeling the relationship between a dependent variable and one or more independent variables. The goal is to find a function that can make the best possible predictions for new, unseen data points. There are several types of regression models that can be applied, depending on the nature of the data and the purpose of the prediction. Some commonly used regression models are linear regression, logistic regression and polynomial regression.
To build a regression model, historical data are used for which both the input variables and the output variable are known. This data is used to train the model, where the model attempts to learn the underlying relationship between the variables. After the model is trained, it can be used to make predictions for new, unseen data points. Regression in machine learning is a powerful tool used in various fields, such as finance, marketing, healthcare and many others. It allows us to gain insights from data and make predictions that can be valuable for decision-making and planning.
