Evaluate Machine Learning Algorithm – leave one out cross validation in R

Evaluating the performance of a machine learning algorithm is an important step in understanding how well it will work on new, unseen data. One popular method for evaluating the performance of an algorithm is called “leave-one-out cross validation” (LOOCV).

In leave-one-out cross validation, the data is divided into n subsets, where n is the number of observations in the data. The algorithm is trained on n-1 observations and tested on the remaining observation. This process is repeated n times, with each observation being left out once. The performance of the algorithm is then averaged over all n iterations.

In R, there are several packages and functions to perform leave-one-out cross validation. The popular package mlr contains a function called resample which can perform leave-one-out cross validation.

One advantage of using leave-one-out cross validation in R is that it can provide a more robust estimate of the performance of a machine learning algorithm, as the performance is averaged over multiple iterations. It also helps to reduce the chances of overfitting, which is when a model is too closely fit to the training data and doesn’t work well on new data.

However, leave-one-out cross validation can be computationally intensive, especially when the sample size is large, as it requires to train the model n times.

Overall, leave-one-out cross validation is a powerful method for evaluating the performance of a machine learning algorithm in R. It can provide a more robust estimate of the algorithm’s performance and can help to reduce the chances of overfitting. However, it can be computationally intensive, so it’s important to consider the sample size before deciding to use this method.

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: Evaluate Machine Learning Algorithm – leave one out cross validation in R.

Evaluate Machine Learning Algorithm – leave one out cross validation in R

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.