> nterpretability is fundamentally not a math or statistics challenge
No, I keep on seeing AI/ML/DS people keep on downplaying statistic.
Statistic interpret things. The majority of the models out there have a one to one, predictor to response, holding all other predictor constant (linear regression, logistic regression, arima, anova, etc..). Statistic inference is a thing. Inference is interpreting. Descriptive statistic is interpreting. Parsimonious is a thing. Experimental design is a thing. Degree of freedom is a thing in statistic.
If you want interpretability do statistic. One of it's tenant is to quantify and live with uncertainty not fitting a curve and lots of coefficient to just predict. Not just classification.
It's a reason why biostat or econometric is a thing. Statistic.
Even the blog cited statistic papers even though it barely mention statistic models in it. ~~And Rudin is a statistician and contributed a lot in statistic.~~ Wrong person (I'm thinking of Rubin for casuality and missingness)
This is not a tribal fight between statistic and ML. This is pointing out that ignoring statistic is a detriment to AI/ML/DS as a field.
I predict that 2020 to 2030 statistic will be coming to AI/ML much more so regardless how much people downplay statistic.
~~Seeing on Dr. Rudin is coming over.~~ I've seen other statistician too. Dr. Loh works took decision tree and added ANOVA and Chisqaure to build parsimonious decision tree.
No, I keep on seeing AI/ML/DS people keep on downplaying statistic.
Statistic interpret things. The majority of the models out there have a one to one, predictor to response, holding all other predictor constant (linear regression, logistic regression, arima, anova, etc..). Statistic inference is a thing. Inference is interpreting. Descriptive statistic is interpreting. Parsimonious is a thing. Experimental design is a thing. Degree of freedom is a thing in statistic.
If you want interpretability do statistic. One of it's tenant is to quantify and live with uncertainty not fitting a curve and lots of coefficient to just predict. Not just classification.
It's a reason why biostat or econometric is a thing. Statistic.
Even the blog cited statistic papers even though it barely mention statistic models in it. ~~And Rudin is a statistician and contributed a lot in statistic.~~ Wrong person (I'm thinking of Rubin for casuality and missingness)
This is not a tribal fight between statistic and ML. This is pointing out that ignoring statistic is a detriment to AI/ML/DS as a field.
I predict that 2020 to 2030 statistic will be coming to AI/ML much more so regardless how much people downplay statistic.
~~Seeing on Dr. Rudin is coming over.~~ I've seen other statistician too. Dr. Loh works took decision tree and added ANOVA and Chisqaure to build parsimonious decision tree.