wordlescraper
imodels
wordlescraper | imodels | |
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4 | 7 | |
0 | 1,293 | |
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0.0 | 8.5 | |
9 months ago | 21 days ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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wordlescraper
- Daily Wordle #421 - Sunday, 14 Aug. 2022
- Only lost once (HOMER) but my 99% just turned back into 100% after hitting 200 played. Anyone else?
- 200 up this morning
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Show & Tell - Ever wonder how your wordle score compares to others or why certain words are harder to guess? Check out my project (with Data Science predictive model for a given Word).
I'm open to feedback on any part of this project, including the Data Science part see Jupyter Notebook here.
imodels
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[D] Have researchers given up on traditional machine learning methods?
- all domains requiring high interpretability absolutely ignore deep learning at all, and put all their research into traditional ML; see e.g. counterfactual examples, important interpretability methods in finance, or rule-based learning, important in medical or law applications
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What would be my best approach given the data I have?
Next, this variable will be your target and you can use various supervised learning models to answer your question. Since interpretation is key, you can use something from here: https://github.com/csinva/imodels or do some black box models and use shab to understand which features contributed most.
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Random Forest Estimation Question
Option 2) fit a model from https://github.com/csinva/imodels on the predicted values of the RF
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UC Berkeley Researchers Introduce ‘imodels: A Python Package For Fitting Interpretable Machine Learning Models
Despite recent breakthroughs in the formulation and fitting of interpretable models, implementations are frequently challenging to locate, utilize, and compare. imodels solves this void by offering a single interface and implementation for a wide range of state-of-the-art interpretable modeling techniques, especially rule-based methods. imodels is basically a Python tool for predictive modeling that is simple, transparent, and accurate. It gives users a straightforward way to fit and use state-of-the-art interpretable models, all of which are compatible with scikit-learn (Pedregosa et al., 2011). These models can frequently replace black-box models while boosting interpretability and computing efficiency without compromising forecast accuracy. Continue Reading
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[D] Looking for open source projects to contribute
Our package imodels is expanding our sklearn-compatible set of interpretable models and always looking for new contributors!
- imodels: a package extending sklearn with state-of-the-art models for interpretable data science (e.g. Bayesian Rule Lists, RuleFit)
- imodels: a package extending sklearn with state-of-the-art interpretable models (e.g. Bayesian Rule Lists, RuleFit) from BAIR [P]
What are some alternatives?
ML-foundations - Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
pycaret - An open-source, low-code machine learning library in Python
facet - Human-explainable AI.
interpret - Fit interpretable models. Explain blackbox machine learning.
Basic-Mathematics-for-Machine-Learning - The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI
shap - A game theoretic approach to explain the output of any machine learning model.
cracking-the-data-science-interview - A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
docarray - Represent, send, store and search multimodal data
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
dopamine - Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
Network-Intrusion-Detection-Using-Machine-Learning - A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach