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In perspective of storing data in databases, storing correlated features is somehow similar to storing redundant information which it may cause wasting of storage and also it may cause inconsistent data after updating or editing tuples.
Should I drop highly correlated features?
For the model to be stable enough, the above variance should be low. If the variance of the weights is high, it means that the model is very sensitive to data. The weights differ largely with training data if the variance is high.
Is correlation good for feature selection?
How does correlation help in feature selection? Features with high correlation are more linearly dependent and hence have almost the same effect on the dependent variable. So, when two features have high correlation, we can drop one of the two features.
Remember, correlation does not imply causation. Correlation coefficients whose magnitude are between 0.9 and 1.0 indicate variables which can be considered very highly correlated. Correlation coefficients whose magnitude are between 0.7 and 0.9 indicate variables which can be considered highly correlated.
What are highly correlated features?
In many datasets we find some of the features which are highly correlated that means which are some what linearly dependent with other features. These features contribute very less in predicting the output but increses the computational cost.
Why is multicollinearity bad?
Multicollinearity makes it hard to interpret your coefficients, and it reduces the power of your model to identify independent variables that are statistically significant. These are definitely serious problems. Multicollinearity affects only the specific independent variables that are correlated.
These correlated columns convey similar information to the learning algorithm and therefore, should be removed.
What happens if variables are highly correlated?
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.
Why is Multicollinearity bad?
Just because your features are correlated does not mean they are not useful, in fact, this correlation could be valuable if your dataset is in fact representative of what is out there “in the wild”.
Which is an example of a highly correlated variable?
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. Random forests can also be used for feature selection by looking at the feature importances of the variable.
How does correlation affect the performance of a model?
Correlated features in general don’t improve models (although it depends on the specifics of the problem like the number of variables and the degree of correlation), but they affect specific models in different ways and to varying extents:
Random forests can be good at detecting interactions between different features, but highly correlated features can mask these interactions. More generally, this can be viewed as a special case of Occam’s razor. A simpler model is preferable, and, in some sense, a model with fewer features is simpler.