Clusterlab is a CRAN package (https://cran.r-project.org/web/packages/clusterlab/index.html) for the routine testing of clustering algorithms. It can simulate positive (data-sets with >1 clusters) and negative controls (data-sets with 1 cluster). Why test...continue reading.
The truth is out there R readers, but often it is not what we have been led to believe. The previous post examined the strong positive results bias in optimism...continue reading.
In the previous parts of the series we demonstrated a positive results bias in optimism corrected bootstrapping by simply adding random features to our labels. This problem is due to...continue reading.
Welcome to part III of debunking the optimism corrected bootstrap in high dimensions (quite high number of features) in the Christmas holidays. Previously we saw with a reproducible code implementation...continue reading.
Some people are very fond of the technique known as ‘optimism corrected bootstrapping’, however, this method is clearly bias and this becomes apparent as we increase the number of features...continue reading.
There are lots of ways to assess how predictive a model is while correcting for overfitting. In Caret the main methods I use are leave one out cross validation, for...continue reading.
The Monti et al. (2003) consensus clustering algorithm is one of the most widely used class discovery techniques in the genome sciences and is commonly used to cluster transcriptomic, epigenetic,...continue reading.
There is interest in bitcoin at the moment because it is displaying signs of steady year to year growth with brief boosts followed by rapid declines. It is considered a...continue reading.
I have been working in high dimensional analysis to predict drug response in rheumatoid arthritis patients and I was concerned to find the procedure called optimism corrected bootstrapping over-fits as...continue reading.