Why should correlated variables be removed?
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The only reason to remove highly correlated features is storage and speed concerns. Other than that, what matters about features is whether they contribute to prediction, and whether their data quality is sufficient.
Why do we need to remove multicollinearity?
Multicollinearity reduces the precision of the estimated coefficients, which weakens the statistical power of your regression model. You might not be able to trust the p-values to identify independent variables that are statistically significant.
Why is correlation important in machine learning?
It gives us the idea about the degree of the relationship of the two variables. If two variables are closely correlated, then we can predict one variable from the other. 2. Correlation plays a vital role in locating the important variables on which other variables depend.
What happens when we have correlated features in our data?
When we have highly correlated features in the dataset, the values in “S” matrix will be small. So inverse square of “S” matrix (S^-2 in the above equation) will be large which makes the variance of Wₗₛ large. So, it is advised that we keep only one feature in the dataset if two features are highly correlated.
There are multiple ways to deal with this problem. The easiest way is to delete or eliminate one of the perfectly correlated features. Another way is to use a dimension reduction algorithm such as Principle Component Analysis (PCA).
What is the impact of correlated independent variables?
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 does PCA remove correlation?
Since 98.6\% of the total variance is captured by the 1st 6 PCA itself, we take only 6 components of PCA and compute a correlation heatmap to overserve the multicollinearity. Hence by reducing the dimensionality of the data using PCA, the variance is preserved by 98.6\% and multicollinearity of the data is removed.
What is correlated variables in machine learning?
The statistical relationship between two variables is referred to as their correlation. A correlation could be positive, meaning both variables move in the same direction, or negative, meaning that when one variable’s value increases, the other variables’ values decrease.
Why is correlation important in data analysis?
Correlation is used to find the relationship between two variables which is important in real life because we can predict the value of one variable with the help of other variables, who is being correlated with it.