I came across this PyData 2018 talk by Lucas Bernadi of Booking.com where he talks about the importance of selection bias for practical applications of machine learning. We can’t just throw data into machines and expect to see any meaning […], we need to think [about this]. I see a strong trend in the practitioners…

# Tag: performance

## ROC, AUC, precision, and recall visually explained

A receiver operating characteristic (ROC) curve displays how well a model can classify binary outcomes. An ROC curve is generated by plotting the false positive rate of a model against its true positive rate, for each possible cutoff value. Often, the area under the curve (AUC) is calculated and used as a metric showing how well…

## Beating Battleships with Algorithms and AI

Past days, I discovered this series of blogs on how to win the classic game of Battleships (gameplay explanation) using different algorithmic approaches. I thought they might amuse you as well : ) The story starts with this 2012 Datagenetics blog where Nick Berry constrasts four algorithms’ performance in the game of Battleships. The resulting levels…

## Open Source Visual Inspector for Neuroevolution (VINE)

In optimizing their transportation services, Uber uses evolutionary strategies and genetic algorithms to train deep neural networks through reinforcement learning. A lot of difficult words in one sentence; you can imagine the complexity of the process. Because it is particularly difficult to observe the underlying dynamics of this learning process in neural network optimization, Uber…

## Simpson’s Paradox: Two HR examples with R code.

Simpson (1951) demonstrated that a statistical relationship observed within a populationâ€”i.e., a group of individualsâ€”could be reversed within all subgroups that make up that population. This phenomenon, where X seems to relate to Y in a certain way, but flips direction when the population is split for W, has since been referred to as Simpson’s…