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#econml β€” Public Fediverse posts

Live and recent posts from across the Fediverse tagged #econml, aggregated by home.social.

  1. My PR to the #EconML #PyWhy #opensource #causalai project was merged! πŸŽ‰ I made a small contribution by allowing a flexible choice of evaluation metric for scoring both the first stage and final stage models in Double Machine Learning (#DML). Before, only the mean square error (MSE) was implemented. But as an ML practitioner "in the trenches" I have found that MSE is hard to interpret and compare across models. My new functions allow that πŸ™‚ #CausalInference #machinelearning #datascience

  2. My PR to the #EconML #PyWhy #opensource #causalai project was merged! πŸŽ‰ I made a small contribution by allowing a flexible choice of evaluation metric for scoring both the first stage and final stage models in Double Machine Learning (#DML). Before, only the mean square error (MSE) was implemented. But as an ML practitioner "in the trenches" I have found that MSE is hard to interpret and compare across models. My new functions allow that πŸ™‚ #CausalInference #machinelearning #datascience

  3. My PR to the #EconML #PyWhy #opensource #causalai project was merged! πŸŽ‰ I made a small contribution by allowing a flexible choice of evaluation metric for scoring both the first stage and final stage models in Double Machine Learning (#DML). Before, only the mean square error (MSE) was implemented. But as an ML practitioner "in the trenches" I have found that MSE is hard to interpret and compare across models. My new functions allow that πŸ™‚ #CausalInference #machinelearning #datascience

  4. My PR to the #EconML #PyWhy #opensource #causalai project was merged! πŸŽ‰ I made a small contribution by allowing a flexible choice of evaluation metric for scoring both the first stage and final stage models in Double Machine Learning (#DML). Before, only the mean square error (MSE) was implemented. But as an ML practitioner "in the trenches" I have found that MSE is hard to interpret and compare across models. My new functions allow that πŸ™‚ #CausalInference #machinelearning #datascience

  5. My PR to the #EconML #PyWhy #opensource #causalai project was merged! πŸŽ‰ I made a small contribution by allowing a flexible choice of evaluation metric for scoring both the first stage and final stage models in Double Machine Learning (#DML). Before, only the mean square error (MSE) was implemented. But as an ML practitioner "in the trenches" I have found that MSE is hard to interpret and compare across models. My new functions allow that πŸ™‚ #CausalInference #machinelearning #datascience