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.
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.
Multicollinearity
- This refers to associations between predictor variables, which you will study later.
Feature selection
- Selecting an optimal set from a pool of given features, many of which might be redundant, becomes an important task.
Coming up
Rahim discussed overfitting in the video above. In the next segment, you will study multicollinearity using which you can reduce the number of variables in your model.
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