# Evaluate Machine Learning Algorithm – repeated kfold 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 “repeated k-fold cross validation.”

In repeated k-fold cross validation, the data is divided into k subsets or “folds” multiple times. The algorithm is trained on k-1 of the folds and tested on the remaining fold. This process is repeated a fixed number of times, with each fold being used as the test set multiple times. The performance of the algorithm is then averaged over all iterations.

In R, there are several packages and function to perform repeated k-fold cross validation. The popular packages are `caret` and `mlr` which contains functions such as `trainControl` and `resample` respectively.

One advantage of using repeated k-fold 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. Also, it is useful when the sample size is small.

However, repeated k-fold cross validation can be computationally intensive, especially when the sample size is large and the number of iterations is high. Also, it’s important to choose the value of k and the number of iterations carefully to ensure that the performance is representative of the population.

Overall, repeated k-fold cross validation is a powerful and flexible 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 and the values of k and the number of iterations should be chosen carefully.

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 – repeated kfold cross validation in R.

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