I was recently discussing bagging resampling versus replicate resampling with a friend. From then on I’ve been particularly interested in understanding the two in a more in-depth way. So, here’s my attempt to explain my newfound knowledge on the subject.
Bagging and replicate resampling, in a nutshell, are two techniques used to estimate the accuracy of a model. Bagging resampling involves randomly shuffling observations and then sampling them in multiple groups of equal size. This, in turn, trains and tests different combinations of data to build up a global accuracy rate of the model. Replicate resampling, on the other hand, is used to identify positive bias that exists in existing data sets. Here, the data is split into two independent samples, one being a training sample and the other a testing sample.
Well naturally, I wanted to know what makes the two different. And interestingly, the key difference lies in the extent of accuracy they bring in. Bagging resampling provides more accurate results than replicate resampling. This is because it takes into consideration the systematic errors that may be present in the data set. Replicate resampling, on the other hand, simply checks for any bias that may exist. The results iti provides are more likely to be evidence of a directional bias.
Now beyond accuracy, another difference between the two is the amount of time they take to produce results. Bagging resampling is usually more time consuming than the replicate resampling technique. A process which can be completed in mere seconds for replicate resampling can take hours or days for the bagging resampling technique. This is most likely because the bagging resampling method requires the verification of accuracy over multiple iterations.
Then there is also the question of cost associated with both the techniques. Bagging resampling is more cost-intensive than replicate resampling. This is due to the fact that it requires the use of computers to build up numerous iterations of the model based on random combinations of the data. Replicate resampling, on the other hand, requires only a single iteration.
So you see, from the above I kind of have an understanding of the two techniques. But I have gone further in-depth to make sure that I can differentiate both. Here are four more sections.
First, a more holistic look at these two methods would be by discussing the advantages and disadvantages of each. The key benefit of the bagging resampling technique is that it produces more reliable and accurate results than replicate resampling. It also provides an idea of how well the model would work on the real data set. On the flip side, the bagging resampling technique can be quite compute-intensive and thus time-consuming. On the other hand, the replicate resampling technique is quite simple and can be done quickly. However, the accuracy of the results provided by the replicate resampling isn’t always as reliable as the results provided by the bagging technique.
Secondly, the threats associated with the two techniques can also be discussed from a more insightful perspective. The biggest threat with bagging resampling is the potential of overfitting, especially when used on a too-small data set. This can lead to inaccurate or unreliable results. Also, the technique of bagging resampling can be further improved if more iterations are carried out with the data set. Replicate resampling, on the other hand, presents the risk of underfitting. This is because the technique checks only for bias in existing data sets and not for the systematic errors that are present in the data sets.
Thirdly, I have also analysed the impact both the techniques have had on the machine learning industry. It is widely accepted that the bagging resampling technique has helped to create more reliable models in recent times. This is because the easy availability of computing-power has made it easier for machine learning engineers to design and rigidly validate multiple versions of a model. Replicate resampling does not offer the same level of accuracy when compared with bagging resampling, but it has an economic advantage for the companies as it requires lesser computational resources.
And lastly, there can be a comparison between the two methods on the basis of their application. Bagging resampling is mostly used in building high-end deep learning models, where the number of features and data points are very large. The technique helps to differentiate the effects of both the bias and variance on the model. Replicate resampling, on the other hand, is mostly used when the feature set is quite small. This is because the technique can quickly check for any bias or discrepancies in the given data set.
So to sum it all up, it is clear that while bagging resampling is more accurate and can provide more reliable results, a lot of time and resources will be invested to obtain those results. Replicate resampling, on the other hand, is much simpler and cheaper but isn’t always the most accurate technique. Hopefully this will give you a better idea of the two techniques and help you decide which one is the right one for you.