#econml — Public Fediverse posts

My PR to the #EconML #PyWhy #opensource #causalai project was merged! 🎉 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 🙂 #CausalInference #machinelearning #datascience

My PR to the #EconML #PyWhy #opensource #causalai project was merged! 🎉 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 🙂 #CausalInference #machinelearning #datascience

My PR to the #EconML #PyWhy #opensource #causalai project was merged! 🎉 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 🙂 #CausalInference #machinelearning #datascience

My PR to the #EconML #PyWhy #opensource #causalai project was merged! 🎉 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 🙂 #CausalInference #machinelearning #datascience

My PR to the #EconML #PyWhy #opensource #causalai project was merged! 🎉 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 🙂 #CausalInference #machinelearning #datascience

I just made my first PR on an #OpenSource project. Ever. Why did that take so long? IDK - I guess all the projects I worked with had everything I needed, and I was too busy to volunteer my time for something I didn't need. The PR is just a small suggestion to improve memory performance in #EconML (#causalml) - we'll see what the repo maintainers think of my hackery, haha...