You've detected a high Variance Inflation Factor (VIF) for one of the variables in your Multiple Linear Regression model. What does this indicate, and how would you proceed?
- High multicollinearity and consider removing or combining variables
- Low multicollinearity
- No multicollinearity
- The variable is not significant
A high VIF indicates high multicollinearity, meaning the variable is highly correlated with other variables in the model. You may consider removing or combining variables, applying regularization, or using dimensionality reduction techniques to address this issue and improve the model's performance.
Loading...
Related Quiz
- An organization wants to develop a system that can identify objects in real-time from video feeds, regardless of the objects' positions or angles in the frames. Which neural network characteristic is crucial for this?
- In the context of healthcare, what is the significance of machine learning models being interpretable?
- The output of a GAN, after training, is a/an ________ that closely resembles the real data.
- You've applied PCA but the variance explained by the first few components is very low. What could be the underlying issue and how might you remedy it?
- How does the Kernel Trick help in dealing with non-linear data in SVM?