Contents
What is difference between Collinearity and correlation?
How are correlation and collinearity different? Collinearity is a linear association between two predictors. Multicollinearity is a situation where two or more predictors are highly linearly related. But, correlation ‘among the predictors’ is a problem to be rectified to be able to come up with a reliable model.
When independent variables are highly correlated, change in one variable would cause change to another and so the model results fluctuate significantly. The model results will be unstable and vary a lot given a small change in the data or model.
When to remove all correlated variables in machine learning?
You do not want to remove all correlated variables. It is only when the correlation is so strong that they do not convey extra information. This is both a function of the strength of correlation, how much data you have and whether any small difference between correlated variables tell you something about the outcome, after all.
Do you use correlation to screen for variables?
In general, the answer is no. However, people do use correlation to screen variables when many variables are included in the model. A few reasons for no: 1. Correlation is just one way to measure how the independent variables correlate to the dependent variables. Higher correlation could be even be caused by random noise!
When to remove features based on correlation with target variable?
What if there is a strong inter-attributes correlation as high as 1 where the correlated attributes are continuous variables, does this mean that these features hold redundant information for the learner? can I safely remove one of them without risking to lose information?
If you retrain the model, the opposite could have happened. You should consider checking VIF (Variance Inflation Factor). Try removing features with higher VIF. Generally, it is preferred that VIF is below 10.