Better than Deep Learning:
Gradient Boosting Machines (GBM) in R
Szilárd Pafka, PhD
Chief Scientist, Epoch (USA)
eRum Conference, Budapest
May 2018
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Disclaimer:
I am not representing my employer (Epoch) in this talk
I cannot confirm nor deny if Epoch is using any of the methods, tools,
results etc. mentioned in this talk
structured/tabular data: GBM (or RF)
very small data: LR
very large sparse data: LR with SGD (+L1/L2)
images/videos, speech: DL
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structured/tabular data: GBM (or RF)
very small data: LR
very large sparse data: LR with SGD (+L1/L2)
images/videos, speech: DL
it depends
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structured/tabular data: GBM (or RF)
very small data: LR
very large sparse data: LR with SGD (+L1/L2)
images/videos, speech: DL
it depends / try them all
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structured/tabular data: GBM (or RF)
very small data: LR
very large sparse data: LR with SGD (+L1/L2)
images/videos, speech: DL
it depends / try them all / hyperparam tuning
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structured/tabular data: GBM (or RF)
very small data: LR
very large sparse data: LR with SGD (+L1/L2)
images/videos, speech: DL
it depends / try them all / hyperparam tuning / ensembles
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structured/tabular data: GBM (or RF)
very small data: LR
very large sparse data: LR with SGD (+L1/L2)
images/videos, speech: DL
it depends / try them all / hyperparam tuning / ensembles
feature engineering
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structured/tabular data: GBM (or RF)
very small data: LR
very large sparse data: LR with SGD (+L1/L2)
images/videos, speech: DL
it depends / try them all / hyperparam tuning / ensembles
feature engineering / other goals e.g. interpretability
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structured/tabular data: GBM (or RF)
very small data: LR
very large sparse data: LR with SGD (+L1/L2)
images/videos, speech: DL
it depends / try them all / hyperparam tuning / ensembles
feature engineering / other goals e.g. interpretability
the title of this talk was misguided
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structured/tabular data: GBM (or RF)
very small data: LR
very large sparse data: LR with SGD (+L1/L2)
images/videos, speech: DL
it depends / try them all / hyperparam tuning / ensembles
feature engineering / other goals e.g. interpretability
the title of this talk was misguided
but so is recently almost every use of the term AI