stat_rethinking_2022
Statistical Rethinking course winter 2022 (by rmcelreath)
interpretable-ml-book
Book about interpretable machine learning (by christophM)
| stat_rethinking_2022 | interpretable-ml-book | |
|---|---|---|
| 13 | 37 | |
| 4,107 | 4,799 | |
| - | - | |
| 1.8 | 4.6 | |
| over 2 years ago | 6 days ago | |
| R | Jupyter Notebook | |
| - | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
stat_rethinking_2022
Posts with mentions or reviews of stat_rethinking_2022.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-05.
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Is there another way to determine the effect of the features other than the inbuilt features importance and SHAP values? [Research] [Discussion]
I would recommend the lectures and book of Statistics Rethinking: https://github.com/rmcelreath/stat_rethinking_2022
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[Q] How is multilevel modelling different from a simple interaction/ moderation term?
Not a direct answer to your question, but I'd highly recommend Richard McElreath's lectures and book for learning multilevel modelling.
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Resources for learning Bayesian stats
Statistical Rethinking is an awesome Bayesian introductory course for people that already know some statistical modeling (i.e. GLM, HLM, ...) from the frequentist side.
- Boss wants me to model a process and tweak the parameters to minimize the response variable. How can I do that? e.g. number of customers waiting in a bank.
- Short Course on Statistics for Lab Scientists?
- Minimální znalost statistiky pro junior Data Scientist / Engineer pozici (ve finančnim sektoru)?
- Need a data set that I can do linear regression on but also apply hierarchical modelling via Bayesian methods.
- Statistical Rethinking (2022 Edition)
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How to be a biz/tech Anthropologist
Either way, try to pick up some computational and especially statistical expertise on the side. If you can't find coursework for it at your uni, I highly recommend McElreath's Statistical Rethinking. He recently started his lecture series for this year, with resources openly available: https://github.com/rmcelreath/stat_rethinking_2022
interpretable-ml-book
Posts with mentions or reviews of interpretable-ml-book.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-18.
- Interpretable Machine Learning – A Guide for Making Black Box Models Explainable
- A Guide to Making Black Box Models Interpretable
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So much for AI
If you're a student, I'd recommend this book :https://christophm.github.io/interpretable-ml-book/
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Best way to make a random forest more explainable (need to know which features are driving the prediction)
Pretty much everyone shows SHAP plots now. Definitely the way to go. Check out the Christoph Molnar book. https://christophm.github.io/interpretable-ml-book/
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Is there another way to determine the effect of the features other than the inbuilt features importance and SHAP values? [Research] [Discussion]
Yes, there are many techniques beyond the two you listed. I suggest doing a survey of techniques (hint: explainable AI or XAI), starting with the following book: Interpretable Machine Learning.
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Which industry/profession/tasks require an aggregate analysis of data representing different physical objects (And how would you call that?)
Ah, alright. It sounds like you're looking for interpretability so I'd suggest this amazing overview of it by Christoph Molnar. If you choose the right models, or the right way of interpreting those, it can help a ton in communicating not only your results, but also what you did to obtain them.
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What skills do I need to really work on?
Not necessarily; decision trees, Naive Bayes, etc., are interpretable. I'd refer to Molnar--specifically his Interpretable Machine Learning text--if you are interested in that subject.
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Random forest vs multiple regression to determine predictor importance.
Consulting something like Interpretable Machine Learning or the documentation of a package like the vip package would also be a really, really good place to start.
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The Rashomon Effect Explained — Does Truth Actually Exist? [13.46]
Just read a book called Interpretable Machine Learning which focuses on analyzing ML models and determine which inputs has more impact in the result.
- Interpretable Machine Learning
What are some alternatives?
When comparing stat_rethinking_2022 and interpretable-ml-book you can also consider the following projects:
stat_rethinking_2020 - Statistical Rethinking Course Winter 2020/2021
shap - A game theoretic approach to explain the output of any machine learning model.
botorch - Bayesian optimization in PyTorch
machine-learning-yearning - Machine Learning Yearning book by 🅰️𝓷𝓭𝓻𝓮𝔀 🆖
stat_rethinking_2023 - Statistical Rethinking Course for Jan-Mar 2023
neural_regression_discontinuity - In this repository, I modify a quasi-experimental statistical procedure for time-series inference using convolutional long short-term memory networks.
pymc-resources - PyMC educational resources
serve - ☁️ Build multimodal AI applications with cloud-native stack
random-forest-importances - Code to compute permutation and drop-column importances in Python scikit-learn models
stat_rethinking_2022 vs stat_rethinking_2020
interpretable-ml-book vs shap
stat_rethinking_2022 vs botorch
interpretable-ml-book vs machine-learning-yearning
stat_rethinking_2022 vs stat_rethinking_2023
interpretable-ml-book vs neural_regression_discontinuity
stat_rethinking_2022 vs pymc-resources
interpretable-ml-book vs serve
interpretable-ml-book vs random-forest-importances