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I think there is good reason that "old" machine learning models are more popular than DNN in the enerprise space. Most of the data is in the tabular format. Wha
by pplonski86 8y ago
I think there is good reason that "old" machine learning models are more popular than DNN in the enerprise space. Most of the data is in the tabular format. What is more, "old" and simple decision tree or linear model are very easy to understand, deploy and are fast. There is for sure clear advantage of having even simple decision tree implemented in the system than making decisions at random.
- mlthoughts2018 8y agoThe main reason though is that these other methods outperform neural nets in tons of different situations. Even just from an accuracy / business success metric point of view, many problems are just better solved with other classes of models, domain-specific feature engineering, etc. It will probably remain so for many decades at least.
- psandersen 8y agoDNN's make good features though, especially if you have time series data or lots of text. I agree that the final model should be a randomforest/xgboost/lightgbm for typical tabular data.
- mlthoughts2018 8y agoI meant that extracting an intermediate layer as a feature embedding and then sticking a classical model on top of it performs worse than curating features through domain-specific expert tuning, for a ton of diverse application domains.