Hits: 54
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.
How to find correlations among feature variables in R
Free Machine Learning & Data Science Coding Tutorials in Python & R for Beginners. Subscribe @ Western Australian Center for Applied Machine Learning & Data Science.
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:
Applied Statistics with R for Beginners and Business Professionals
Data Science and Machine Learning Projects in Python: Tabular Data Analytics
Data Science and Machine Learning Projects in R: Tabular Data Analytics
Python Machine Learning & Data Science Recipes: Learn by Coding