Hi all! I recently posted a short survey to find out what types of models people are using and since there aren’t too many more results coming in I have decided to publish the results.
I will now rank the types of models in order of popularity*;
- Gradient Boosting (XGBoost, Catboost, etc.) - 69.6%
- Keras Neural Network - 52.2%
- Random Forest - 17.4%
- AutoML - 4.3%
- Ridge - 4.3%
- ElasticNet - 4.3%
- Naive-Bayes - 4.3%
- PyTorch NN - 4.3%
- Jax/Flax NN - 4.3%
- Genetic - 4.3%
- Linear - 4.3%
*Percentages do not add up to 100% due to multi-model/ensembling
It is also interesting to note that 47.8% of people stated that they used an ensembled model.
The final question was a bit more varied, with only a handful of people training on Nomi since its release. Feature neutralisation was also hit and miss with some users reporting that they had stopped neutralising in order to improve results.
I hope this was useful/interesting to someone other than me!