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I think one of the hardest parts of ML is not learning how to write it but learning how to pick features and clean data. So, assuming this is a run down of typ
by zodPod 9y ago
I think one of the hardest parts of ML is not learning how to write it but learning how to pick features and clean data. So, assuming this is a run down of types of ML algorithms (which is how most "learn ML" things seem to go) this is not going to be much more helpful for someone who is still trying to figure it all out. It's important to know these things but knowing the actual algorithms is kind of the low hanging fruit. Data organization and features are the real complex pieces of most ML.
- platz 9y agoI think people are drawn to ML because they've heard they do not need to pick features because the algorithms will find the features for them.
- pakl 9y agoCleaning data eliminates the driving force to develop truly autonomous algorithms.
- kmax12 9y agoThis is absolutely correct! After spending a lot of time trying to understand why building machine learning models is so difficult, I came to the same conclusion that "feature engineering" is the key to building high performing models. While feature engineering's importance is generally recognized [0], it's unfortunate that there aren't more tools and formal methods for applying it. Personally, I am a developer of an open source python library called Featuretools (https://github.com/featuretools/featuretools/ https://github.com/featuretools/featuretools/) that is trying to change this for tabular and multi-table datasets. We are working hard to make automated feature engineering available to everyone and have a list of demos for people to try here: https://www.featuretools.com/demos https://www.featuretools.com/demos. It's also worth noting that deep learning is changing the need for feature engineering. However, it primarily works in cases where you don't need interpretable features and you have plenty of data. This means that it's biggest success have been in images, audio, and text problems. For all other use cases feature engineering is still a necessary step for applying machine learning. [0] https://bit.ly/things_to_know_ml https://bit.ly/things_to_know_ml
- tgarma1234 9y agoThis comment/repo should be it's own HN post as it is vastly more interesting than the post you are commenting on. Thanks for the links.