Row 57549

Row ID: 57549 | Dataset Entry | Axioma AXP Content Repository

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It really depends on the project. Usually I explain to non-ML people that there is a reward curve that looks something like log(x). We can do linear or logistic regression and get an 80% solution most of the time, at the cost of a data pipeline. We can do more work and use an appropriate off the shelf medium sized solution, like xgboost. That might get us to 87%. If that’s not good enough, that’s where things get costly. We can train a vanilla deep learning model, but we need a lot more data and compute. Maybe we get to 91%. Or we can develop something bespoke via research, which is years of time and likely a lot of data and compute and risk. That might be 94% if we hit it out of the park.

In most settings linear models or xgboost is good enough for the execs.

FieldValue
text It really depends on the project. Usually I explain to non-ML people that there is a reward curve that looks something like log(x). We can do linear or logistic regression and get an 80% solution most of the time, at the cost of a data pipeline. We can do more work and use an appropriate off the shelf medium sized solution, like xgboost. That might get us to 87%. If that’s not good enough, that’s where things get costly. We can train a vanilla deep learning model, but we need a lot more data and…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-23
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{
  "text": "It really depends on the project. Usually I explain to non-ML people that there is a reward curve that looks something like log(x). We can do linear or logistic regression and get an 80% solution most of the time, at the cost of a data pipeline. We can do more work and use an appropriate off the shelf medium sized solution, like xgboost. That might get us to 87%. If that’s not good enough, that’s where things get costly. We can train a vanilla deep learning model, but we need a lot more data and compute. Maybe we get to 91%. Or we can develop something bespoke via research, which is years of time and likely a lot of data and compute and risk. That might be 94% if we hit it out of the park.\n\nIn most settings linear models or xgboost is good enough for the execs.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-23",
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