You have a dataset with a high degree of multicollinearity. What steps would you take to address this before building a Multiple Linear Regression model?
- Apply feature selection or dimensionality reduction techniques
- Ignore it
- Increase the size of the dataset
- Remove all correlated variables
Multicollinearity can be addressed by applying feature selection techniques like LASSO or using dimensionality reduction methods like Principal Component Analysis (PCA). These techniques help in removing or combining correlated variables, reducing multicollinearity and improving the model's stability.
Loading...
Related Quiz
- What is multicollinearity in the context of Multiple Linear Regression?
- A company wants to segment its customers based on their purchasing behavior. They have a fair idea that there are around 5 distinct segments but want to confirm this. Which clustering algorithm might they start with?
- When using transfer learning, what part of the pre-trained model is typically fine-tuned for the new task?
- One method to mitigate multicollinearity is to apply ___________ regression, which adds a penalty term to the loss function.
- The _________ method in Cross-Validation ensures that each fold maintains the same proportion of different classes as the complete dataset.