End-to-End Machine Learning: rsquared metric in R

When training a machine learning model, it’s important to evaluate its performance to understand how well it will work on new, unseen data. One common way to evaluate the performance of a model for regression problems is by using a metric called “R-squared” (R²)

R-squared is a measure of how well the model’s predictions fit the true values. It is calculated by taking the ratio of the variance of the predicted values and the total variance of the true values. A higher R-squared value indicates a better model performance, it ranges between 0 and 1, 1 means the model explains all the variability of the response data around its mean.

In R, there are several ways to calculate R-squared, and several libraries such as caret, mlr, etc. which provide functions to calculate R-squared. Some of the most popular functions are rsq(), rsquared() and summary() that can be used to calculate R-squared.

It’s important to note that R-squared values is a good metric for evaluating the performance of a model when the data is linear, it measures how much of the variability in the data is explained by the model. However, when the data is not linear, other metrics such as RMSE should be used to evaluate the performance of the model.

Overall, R-squared is a useful metric for evaluating the performance of a machine learning model for regression problems, it measures how well the model’s predictions fit the true values and how much of the variability in the data is explained by the model. It ranges between 0 and 1, a higher R-squared value indicates a better model performance, but it’s important to use other metrics when the data is not linear.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: End-to-End Machine Learning: rsquared metric in R.

End-to-End Machine Learning: rsquared metric in R

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