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Regression models predict a continuous output variable from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Regression models are a fundamental tool in data science for predicting continuous output variables. In simple terms, this means that regression models are used when the target variable we want to predict is a number, rather than a category or class. For example, if we want to predict the price of a house based on its features, such as size, location, and number of bedrooms, we would use a regression model. The goal of a regression model is to find the relationship between the input variables (also known as features) and the output variable. This relationship is typically represented by a mathematical equation that describes how the input variables influence the output variable. The process of building a regression model involves training the model on a dataset where both the input variables and the output variable are known. The model then learns from this data and uses this knowledge to make predictions on new, unseen data. One key aspect of regression models is that they assume a linear relationship between the input variables and the output variable. This means that the model assumes that the effect of each input variable on the output variable is constant and additive. While this assumption may not always hold true in practice, linear regression remains a powerful and widely used technique in data science due to its simplicity and interpretability. In addition to linear regression, there are many other types of regression models that can be used to predict continuous output variables. For example, polynomial regression allows for non-linear relationships between the input variables and the output variable, while ridge regression and lasso regression are used to prevent overfitting in cases where there are many input variables.- Regression models are a versatile and powerful tool in data science for predicting continuous output variables. By understanding the relationships between input variables and output variables, we can make accurate predictions and gain valuable insights from our data.
