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Is multicollinearity a problem in prediction?
Multicollinearity affects the coefficients and p-values, but it does not influence the predictions, precision of the predictions, and the goodness-of-fit statistics.
Does correlation affect prediction?
A positive correlation is one in which variables go up or down together, producing an uphill slope. Any type of correlation can be used to make a prediction. However, a correlation does not tell us about the underlying cause of a relationship.
Does Collinearity affect accuracy?
Here it is important to point out that multicollinearity does not affect the model’s predictive accuracy. The model should still do a relatively decent job predicting the target variable when multicollinearity is present.
What happens when predictor variables are highly correlated?
That is, think about the system you are studying and all of the extraneous variables that could influence the system. When predictor variables are correlated, the precision of the estimated regression coefficients decreases as more predictor variables are added to the model.
Any help is appreciated. Perform a PCA or MFA of the correlated variables and check how many predictors from this step explain all the correlation. For example, highly correlated variables might cause the first component of PCA to explain 95% of the variances in the data. Then, you can simply use this first component in the model.
I suggest you check correlation first and possibly also the variance inflation factor from linear regression. This should give you a good idea on if the features are too strongly correlated to be considered jointly, e.g. in linear regression. Linear regression could also be a good baseline model here.
Is it possible to use only one highly correlated variable?
Clearly says that all three columns are positively & highly correlated and hence it is viable to use only one variable instead of all three as the information that exists in or or is good enough to compute . Challenge is, any machine learning models cannot automatically identify these highly correlated variables and pick only one of these.