Should highly correlated variables be removed?
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In a more general situation, when you have two independent variables that are very highly correlated, you definitely should remove one of them because you run into the multicollinearity conundrum and your regression model’s regression coefficients related to the two highly correlated variables will be unreliable.
Can PCA handle multicollinearity?
It affects the performance of regression and classification models. PCA (Principal Component Analysis) takes advantage of multicollinearity and combines the highly correlated variables into a set of uncorrelated variables. Therefore, PCA can effectively eliminate multicollinearity between features.
To remove the correlated features, we can make use of the corr() method of the pandas dataframe. The corr() method returns a correlation matrix containing correlation between all the columns of the dataframe.
Is high correlation good?
Understanding Correlation The possible range of values for the correlation coefficient is -1.0 to 1.0. In other words, the values cannot exceed 1.0 or be less than -1.0. A correlation of -1.0 indicates a perfect negative correlation, and a correlation of 1.0 indicates a perfect positive correlation.
Does PCA remove Collinearity?
Hence by reducing the dimensionality of the data using PCA, the variance is preserved by 98.6\% and multicollinearity of the data is removed.
Can PCA be applied on correlation matrix?
The goal of PCA is to summarize the correlations among a set of observed variables with a smaller set of linear combinations. Some software programs allow you to use a correlation or covariance matrix as an input data set. This comes in very handy if you either don’t have the original data or have missing data.
What happens if correlation is high?
High correlation among predictors means you ca predict one variable using second predictor variable. This results in unstable parameter estimates of regression which makes it very difficult to assess the effect of independent variables on dependent variables.
How high is too high correlation?
Correlation coefficients whose magnitude are between 0.7 and 0.9 indicate variables which can be considered highly correlated. Correlation coefficients whose magnitude are between 0.5 and 0.7 indicate variables which can be considered moderately correlated.