DashBot-3.0
Geometry Dash bot to play & finish levels - Now training much faster! (by MCJack123)
interpret
Fit interpretable models. Explain blackbox machine learning. (by interpretml)
DashBot-3.0 | interpret | |
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2 | 6 | |
41 | 6,022 | |
- | 0.9% | |
0.0 | 9.7 | |
about 2 years ago | 7 days ago | |
C++ | C++ | |
GNU General Public License v3.0 only | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
DashBot-3.0
Posts with mentions or reviews of DashBot-3.0.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-16.
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Hello, dev.to!
Outside of ComputerCraft coding, I also like to make various little utilities, apps and devices. One of those was DashBot, a genetic machine learning bot that plays Geometry Dash. iRCON, an iOS app for remotely administering Minecraft servers, has been on my mind recently, and I'm contemplating putting it on the App Store. I also gained some notoriety in the game music ripping scene for my UnkrawerterGBA project, as well as my work on ripping music from the Club Penguin DS games. My favorite non-ComputerCraft project recently has been my MIDI synthesizer/sound generator, which taught me a lot about developing for microcontrollers.
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Some reverse-engineering for Geometry Dash's memory structures
I've been working on DashBot a bit recently, and one thing that annoyed me was that my algorithm for detecting portal entry was not very good and required the user to input each portal type. I ended up spending some time looking for values in the memory, and ended up with a whole bunch of useful offsets. Furthermore, the location of these were next to the X and Y coordinates, hinting that a good portion of the game status is located in one central structure.
interpret
Posts with mentions or reviews of interpret.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-25.
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[D] Alternatives to the shap explainability package
Maybe InterpretML? It's developed and maintained by Microsoft Research and consolidates a lot of different explainability methods.
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What Are the Most Important Statistical Ideas of the Past 50 Years?
You may also find Explainable Boosting Machines interesting: https://github.com/interpretml/interpret
They're a bit like a best of both worlds between linear models and random forests (generalized additive models fit with boosted decision trees)
Disclosure: I helped build this open source package
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[N] Google confirms DeepMind Health Streams project has been killed off
Microsoft Explainable Boosting Machine (which is a Gaussian Additive Model and not a Gradient Boosted Trees 🙄 model) is a step in that direction https://github.com/interpretml/interpret
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[Discussion] XGBoost is the way.
Also I'd recommend everyone who works with xgboost to give EBM's a try! They perform comparably (except in the case of extreme interactions) but are actually interpretable! https://github.com/interpretml/interpret/ Beside that they since on runtime they're practically a lookup table they're very quick (at the cost of longer training time).
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[D] Generalized Additive Models… with trees?
Open source code by Microsoft: https://github.com/interpretml/interpret (called EBM in this implementation).
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Machine Learning with Medical Data (unbalanced dataset)
If it's not an image, have a go at Microsoft's Explainable Boosting Maching) https://github.com/interpretml/interpret which is not a GBM but a GAM (Gradient Boosting Machine vs Gradient Additive Model). This will also give you explanation via SHAP or LIME values.
What are some alternatives?
When comparing DashBot-3.0 and interpret you can also consider the following projects:
Mega-Hack-v5
shap - A game theoretic approach to explain the output of any machine learning model.