How to find correlations among feature variables in R

Correlation is a statistical measure that shows the relationship between two or more variables. In R, there are various ways to find the correlation among feature variables.

One of the most common ways is to use the “cor()” function. This function calculates the correlation between two or more variables and returns the correlation coefficients in a matrix format. The correlation coefficient ranges from -1 to 1, where -1 indicates a strong negative correlation, 0 indicates no correlation, and 1 indicates a strong positive correlation.

Another way to find correlations among feature variables in R is through visualization. One popular visualization method is using scatter plots, where each variable is plotted on the x-axis and y-axis. If the points on the scatter plot form a linear pattern, it indicates a positive correlation, if the points form an inverted “V” shape, it indicates a negative correlation, and if the points are randomly scattered, it indicates no correlation.

Additionally, you can also use a technique called heatmap to find correlation among feature variables. This technique uses color-coded cells to represent the correlation coefficients. The cells with darker colors indicate a stronger correlation and the cells with lighter colors indicate a weaker correlation.

In summary, finding correlation among feature variables in R can be done by using the “cor()” function, scatter plots, and heatmaps. Each method has its own advantages and can be used depending on the data and the purpose of the analysis.

In this Applied Machine Learning Recipe, you will learn: How to find correlations among feature variables in R.