Include Interaction in Regression using R

Let’s say X1 and X2 are features of a dataset and Y is the class label or output that we are trying to predict. Then, If X1 and X2 interact, this means that the effect of X1 on Y depends on the value of X2 and vice versa then where is the interaction between features of the dataset. Now that we know that if our dataset contains interaction or not. We should also know when to take interaction into account in our model for better precision or accuracy. We are going to implement this using the R language.

Should We Include Interaction in Our Model? 

There are two questions you should ask before including interaction in your model: 

• Does this interaction make sense conceptually?
• Is the interaction term statistically significant? Or, whether or not we believe the slopes of the regression lines are significantly different.

In this article, we will look into what is Interaction, and should we use interaction in our model to get better results or not.