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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.
Which is the correct value for the correlation coefficient?
The correlation coefficient is a value that indicates the strength of the relationship between variables. The coefficient can take any values from -1 to 1. The interpretations of the values are: -1: Perfect negative correlation. The variables tend to move in opposite directions (i.e., when one variable increases, the other variable decreases).
How to do PCA of highly correlated variables?
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.
How to highlight the most correlated variables in R?
Use the corr_var () function if you want to focus on the correlation of one variable against all others, and return the highest ones in a plot: corr_var (dat, # name of dataset mpg, # name of variable to focus on top = 5 # display top 5 correlations)
However, when independent variables are correlated, it indicates that changes in one variable are associated with shifts in another variable. The stronger the correlation, the more difficult it is to change one variable without changing another.
I’d like to ask everyone a question about how correlated features (variables) affect the classification accuracy of machine learning algorithms. With correlated features I mean a correlation between them and not with the target class (i.e the perimeter and the area of a geometric figure or the level of education and the average income).