Chapter 2: Applying Linear Regression
Linear regression requires the variables to have a relationship among them to be useful to build a predictive model. We will learn, in this and the following chapters, several statistical techniques to approve the data for a regression model. We will use a portion of the data to design and build the regression. Then, we will apply the other portion of the data to the model for testing. The final step is to load the model for prediction, and we are going to use our experience to see whether the regression is returning results that make sense for the purposes of our experiment or application. Linear regression is a supervised machine learning algorithm, as it needs continuous surveillance to verify that it continues to give results that make sense. If it does not, the algorithm needs a training update.
Linear regression has the following machine learning requirements:
- Defining the model from known data
- Training the model to see whether...
