Moving from SLR to MLR: New Considerations

When moving from a simple linear regression model to a multiple linear regression model, you have to look at a few things.

Let's hear Rahim explain them in the following video.

The new aspects to consider when moving from simple to multiple linear regression are as follows:

1. Overfitting
   • As you keep adding variables, the model may become far too complex.
   • It may end up memorising the training data and, consequently, fail to generalise.
   • A model is generally said to overfit when the training accuracy is high while the test accuracy is very low.
2. Multicollinearity
   • This refers to associations between predictor variables, which you will study later.
3. Feature selection
   • Selecting an optimal set from a pool of given features, many of which might be redundant, becomes an important task.