End-to-End Machine Learning: rmse 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 is by using a metric called “root mean squared error” (RMSE).

RMSE is a measure of how much the model’s predictions differ from the true values. It is calculated by taking the square root of the average of the squared differences between the predicted values and the true values. A lower RMSE value indicates a better model performance.

In R, there are several ways to calculate RMSE, and several libraries such as caret, mlr, etc. which provide functions to calculate RMSE. Some of the most popular functions are rmse(), sqrt() and mean((pred-obs)^2) that can be used to calculate RMSE.

RMSE is commonly used in regression problems, where the goal is to predict a continuous variable. It’s a good metric to evaluate the performance of models that predict continuous variables such as linear regression, decision trees, and neural networks.

It’s important to note that a lower RMSE value indicates a better model performance and it’s important to compare the RMSE value to other models to understand the performance of the model.

Overall, RMSE is a useful metric for evaluating the performance of a machine learning model in regression problems, where the goal is to predict a continuous variable. It measures how much the model’s predictions differ from the true values and a lower RMSE value indicates a better model performance. It’s important to compare the RMSE value to other models to understand the performance of the model.

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: rmse metric in R.

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

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