Random Forest on Small Numbers of Observations

Neil Saunders takes us through an interesting problem:

A recent question on Stack Overflow [r] asked why a random forest model was not working as expected. The questioner was working with data from an experiment in which yeast was grown under conditions where (a) the growth rate could be controlled and (b) one of 6 nutrients was limited. Their dataset consisted of 6 rows – one per nutrient – and several thousand columns, with values representing the activity (expression) of yeast genes. Could the expression values be used to predict the limiting nutrient?

The random forest was not working as expected: not one of the nutrients was correctly classified. I pointed out that with only one case for each outcome, this was to be expected – as the random forest algorithm samples a proportion of the rows, no correct predictions are likely in this case. As sometimes happens the question was promptly deleted, which was unfortunate as we could have further explored the problem.

Neil decided to explore the problem further regardless and came to some interesting conclusions.

Related Posts

Building an Image Classifier with PyTorch

Rogier van der Geer shows how you can use PyTorch to build out a Convolutional Neural Network for image classification: The tool that we are going to use to make a classifier is called a convolutional neural network, or CNN. You can find a great explanation of what these are right here on wikipedia. But we […]

Read More

xgboost and Small Numbers of Subtrees

John Mount covers an interesting issue you can run into when using xgboost: While reading Dr. Nina Zumel’s excellent note on bias in common ensemble methods, I ran the examples to see the effects she described (and I think it is very important that she is establishing the issue, prior to discussing mitigation).In doing that I ran into one more […]

Read More

Categories

June 2019
MTWTFSS
« May Jul »
 12
3456789
10111213141516
17181920212223
24252627282930