In this third and last blog post about machine learning strategy I will talk about the problems of different distributions of train, development and test set and about learning from multiple tasks. [Read more...]

This is the second blog post about machine learning strategy. It is about human-level performance, bias and variance (tradeoff) and how to improve your algorithm iteratively. [Read more...]

Machine Learning Strategy is about how to tackle machine learning tasks strategically. In three blog posts I will try to give an introduction into this topic and I also hope for some comments and opinions on this topic. [Read more...]

Several R packages that use different methods are out there for using gradient boosting methods. The three most famous ones are currently xgboost, catboost and lightgbm. I want to compare these three to find out which is the best one in their default mode without tuning. These algorithms are not ... [Read more...]

xgboost is the most famous R package for gradient boosting and it is since long time on the market. In one of my publications, I created a framework for providing defaults (and tunability measures) and one of the packages that I used there was xgboost. The results provided a default ... [Read more...]

Here an overview table of statistical tests that I use for the consultation of medical doctors. Easy-to-use and easy to understand. I usually also hand it over to them. (PDF) [Read more...]

Let’s compare the two popular R packages for machine learning mlr and caret. caret is longer on the market, its first CRAN release seems to be from 2007, while mlr came to CRAN on 2013. As for now, caret seems to be more popular, according to cranlogs caret was downloaded 178029 times ... [Read more...]

In this article I will present a benchmark analysis of different implementations of support vector machines in R. The three packages that I will compare are the most popular package e1071, the also well known package kernlab and the less well known package liquidSVM. [Read more...]

The support vector machine (SVM) is a very different approach for supervised learning than decision trees. In this article I will try to write something about the different hyperparameters of SVM. [Read more...]

Random forest is one of the standard approaches for supervised learning nowadays. One of its advantages is that it does not require tuning of the hyperparameters to perform good. But is that really true? Maybe we are not only interested in a good model but in the best model we ... [Read more...]

I just updated the code from a previous post where I analysed the download statistics of different random forest packages in R, see the code at the bottom of the article. I calculated the number of cran downloads in march 2016 and march 2017. Standard random forest The number of download of ... [Read more...]