This seems to be “methods used”. Would arithmetic be at 100%?
2020 Kaggle survey: regression, decision trees and ensembles dominate in ML applications.pic.twitter.com/dZTzI7wskV
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The salient fact is that, counter to perception, most practical applications of ML don't use deep learning. (Use of arithmetic is universal, and therefore uninformative and not surveyed.)
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It says "Methods and Algorithms **Usage**" - linear and logistic regression may dominate in usage but obviously not in expressivity. Hardly surprising given 1) beginning ML practitioners will take up linear and logistic regression to begin & 2) kaggle has been around since 2010.
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No, actually. The point is that for most applications boosting, random forests and regression work better than deep learning.
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I’d expect an appreciation for a csv data set to also bring an appreciation for the Oxford comma.
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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The industry mostly uses ML on structured, tabular datasets, which are ubiquitous, mature and rich, and hence simple regressions or boosting tree-based methods gives most bang for a buck. On the flipside, DL on unstructured data is expensive and works only in much bigger scale.
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It's not just that. For the vast majority of tabular data use cases Boosted Trees easily outperform NNs.
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Or as Baron Schwartz once wrote: "When you’re fundraising, it’s AI. When you’re hiring, it’s ML. When you’re implementing, it’s linear regression.”
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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ayes would roll in his grave reading this figure.
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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Additions and subtractions are far more used than calculus... So what???
Kiitos. Käytämme tätä aikajanasi parantamiseen. KumoaKumoa
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