Evaluate Machine Learning Algorithm in R – bootstrap 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 “bootstrapping.”
Bootstrapping is a resampling method that creates multiple new samples of data by randomly selecting data points from the original sample with replacement. This allows you to estimate the performance of your algorithm on different subsets of the data and get a better understanding of its variability.
In R, the “boot” package provides an implementation of bootstrap. This package includes functions that can be used to create bootstrap samples and evaluate the performance of a machine learning algorithm.
One advantage of using bootstrap in R is that it can provide a robust estimate of the performance of a machine learning algorithm, even when the sample size is small. It can also be used to estimate the confidence intervals of the performance measures which can be useful to understand the uncertainty of the algorithm performance.
However, bootstrap can be computationally intensive when the sample size is large, so it’s important to make sure that the sample size is big enough to ensure that the results are representative of the population but not too large to avoid long computational times.
Overall, bootstrap is a powerful and flexible method for evaluating the performance of a machine learning algorithm in R. It can provide a robust estimate of the algorithm’s performance and can be used to estimate the uncertainty of the performance measures. However, it’s important to keep in mind that it can be computationally intensive and the sample size 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 in R – bootstrap in R.