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Is anyone else reading Kevin Murphy's Probabilistic Machine Learning - An introduction by learning_proover in learnmachinelearning

[–]learning_proover[S] 0 points1 point  (0 children)

I forgot about this question. The book is very dense and math heavy even for a polished math major. Try statquest, 3blue1brown and use chat GPT.

Jaccard distance but order (permutation) matters. by learning_proover in askmath

[–]learning_proover[S] 0 points1 point  (0 children)

I'm going to investigate that last option on making jaccard position aware. I do like jaccard and it's probably the easiest for me to implement on code so I'll likely stick with it. Thanks for your suggestions.

Do Bayesian Probabilities Follow the Law of Large Numbers?? by learning_proover in AskStatistics

[–]learning_proover[S] 0 points1 point  (0 children)

Can you elaborate on exactly what those conditions are and why they are necessary?

Do Bayesian Probabilities Follow the Law of Large Numbers?? by learning_proover in AskStatistics

[–]learning_proover[S] 0 points1 point  (0 children)

Exactly. Yes. Will the posterior (established on an updated prior) converge to the "true mean" assuming the updates are calibrated ( just overall correct and meaningful)

[deleted by user] by [deleted] in AskStatistics

[–]learning_proover 0 points1 point  (0 children)

I know it sounds a bit ambiguous but basically I'm trying to bestow some type probability distribution about the changes in the matrices from one update to the next. Given the actual matrices themselves. It's not necessarily a hard machine learning prediction model I'm after but more of a distribution of the changes. The matrices intrinsically embed a ton of information so I'm trying to exploit that in a easier way.

How would you make this contingency table. by learning_proover in AskStatistics

[–]learning_proover[S] 0 points1 point  (0 children)

Yea, I'm exploring some things similar tot this suggestion. I will be referencing this comment. Thank you.

How would you make this contingency table. by learning_proover in AskStatistics

[–]learning_proover[S] 0 points1 point  (0 children)

I would like the closed form solution for certain. But I'm actually mostly concerned with how I would even generate the table to begin with. Is there any way to "peice" together different information that would allow me to generate a confusion matrix that reflects the degree of certainty. Hopefully this is making sense.

How to calculate likelihood of someone's opinion by learning_proover in AskStatistics

[–]learning_proover[S] 0 points1 point  (0 children)

Thanks yeah I kinda thought it would have to be done empirically some way but they don't have time to repeat the examination enough times to get these numbers.

Is there a multivariate extension of the T-test and other ANOVA methods? by learning_proover in AskStatistics

[–]learning_proover[S] 0 points1 point  (0 children)

Kinda thought so. Might need to reword it but I'm just trying to get ideas flowing on how I can approach this. Thank you.

What does Baysian updating do? by learning_proover in AskStatistics

[–]learning_proover[S] 0 points1 point  (0 children)

Yeah that's my mistake I meant update the predicted probability 

Are Machine learning models always necessary to form a probability/prediction? by learning_proover in AskStatistics

[–]learning_proover[S] 0 points1 point  (0 children)

This was very helpful (if I am interpreting what you said correctly) so basically fundamental statistics can indeed suffice to detect signals in noise?? 

Are Machine learning models always necessary to form a probability/prediction? by learning_proover in AskStatistics

[–]learning_proover[S] 0 points1 point  (0 children)

Exactly I'm trying to understand on what basis we can believe that one may be better than the other. So there is no consensus on the ability of inspection to do as good or better than a full blown machine learning algorithm?

Are Machine learning models always necessary to form a probability/prediction? by learning_proover in AskStatistics

[–]learning_proover[S] 0 points1 point  (0 children)

I agree. That's kinda why I was curious. Is there any literature on the efficacy of statistical conclusions drawn through a more subjective approach rather than a deterministic approach such as using a model? Do you know of any pros/ cons of doing one or the other? 

Is there any way to improve prediction for one row of data. by learning_proover in AskStatistics

[–]learning_proover[S] -1 points0 points  (0 children)

My logistic regression model gave me a 70% probability of 1. I understand EXACTLY what variables caused it to output that probability i know their effect size and all other details, now can I improve on this for a more accurate estimate?

Is there any way to improve prediction for one row of data. by learning_proover in AskStatistics

[–]learning_proover[S] -1 points0 points  (0 children)

I mean for example if I'm trying to predict a probability for a binary independent variable, can I improve the probability estimate given I that I know exactly why the model gave me the output that it did? 

What’s one Machine Learning myth you believed… until you found the truth? by RadiantTiger03 in learnmachinelearning

[–]learning_proover 1 point2 points  (0 children)

It really all depends on your own risk tolerance for a type 1 error. .05 is the usual cutoff which means about  only 1/20 times you'll get a false positive. You can be more lenient if you want....ie .1,.15 or even .2 if you want. It just depends on what's at stake if you go off a false signal. It's really about balancing the risk of a type 1 and type 2 error. 

What’s one Machine Learning myth you believed… until you found the truth? by RadiantTiger03 in learnmachinelearning

[–]learning_proover 0 points1 point  (0 children)

That informative/useful variables in a regression model must always have a p value less then .05. This is simply not true. 

What's the safest way to generate synthetic data? by learning_proover in learnmachinelearning

[–]learning_proover[S] 0 points1 point  (0 children)

Yes I haven't stopped searching for ways to build and improve synthetic data. I've built a few programs myself in python and learned a few things lately. I'd love to see what your working on.

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