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Explain the relation/difference between covariance and correlation.
The covariance is a measure for how two variables are related to each other, i.e., how two variables vary with each other.
The values of correlation are standardized but covariance values are not. The correlation coefficient can be obtained by dividing the covariance of the variables by the product of their standard deviation values. Standard deviation measures the variability of datasets absolutely. When it is divided by the standard deviation it falls in the range of -1 to +1, which is the range of correlation values. The normalized form of covariance is correlation.
Running random forest algorithm with one variable