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The potential solutions include the following:
- Remove some of the highly correlated independent variables.
- Linearly combine the independent variables, such as adding them together.
- Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.
What are the techniques used to detect the multicollinearity in the dataset?
A measure that is commonly available in software to help diagnose multicollinearity is the variance inflation factor (VIF). Variance inflation factors (VIF) measures how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related.
How do you work out if there is a correlation between two variables?
How to Calculate a Correlation
- Find the mean of all the x-values.
- Find the standard deviation of all the x-values (call it sx) and the standard deviation of all the y-values (call it sy).
- For each of the n pairs (x, y) in the data set, take.
- Add up the n results from Step 3.
- Divide the sum by sx ∗ sy.
What are examples of correlation?
Positive Correlation Examples in Real Life
- The more time you spend running on a treadmill, the more calories you will burn.
- Taller people have larger shoe sizes and shorter people have smaller shoe sizes.
- The longer your hair grows, the more shampoo you will need.
How are correlations measured in a dataset?
Correlations are measured between only 2 variables at a time. Therefore, for datasets with many variables, computing correlations can become quite cumbersome and time consuming.
A correlation plot (also referred as a correlogram or corrgram in Friendly ( 2002)) allows to highlight the variables that are most (positively and negatively) correlated. Below an example with the same dataset presented above: The correlogram represents the correlations for all pairs of variables.
How to fit a regression with correlated data?
First, we use the glm () function to fit a simple logistic regression model using the “fragile_families” data. Since we have a binary outcome variable, “family = binomial” is used to specify that logistic regression should be used. We also use tidy () from the “broom” package to clean up the model output.
How to handle correlation features in Kaggle kernel?
The produced data set contains a lot of strongly correlated features. In this kernel I analyse these correlation. Furthermore, I implement different strategies to reduce the number of features and test these strategies with respect to the achievable prediction accuracy of different classifiers.