xgboost-survival-embeddings
Improving XGBoost survival analysis with embeddings and debiased estimators (by loft-br)
skope-rules
machine learning with logical rules in Python (by scikit-learn-contrib)
| xgboost-survival-embeddings | skope-rules | |
|---|---|---|
| 3 | 3 | |
| 308 | 591 | |
| 3.2% | 0.7% | |
| 0.0 | 0.0 | |
| 9 months ago | 4 months ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | 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.
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.
xgboost-survival-embeddings
Posts with mentions or reviews of xgboost-survival-embeddings.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-24.
- Churn/Retention prediction without machine learning
- Show HN: Better XGBoost Survival Analysis via embeddings and debiased estimators
-
Forecasting multiple time series ideas
Here’s a great package for survival analysis in python
skope-rules
Posts with mentions or reviews of skope-rules.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-06.
- [D] Convert a ML model into a rule based system
-
Churn/Retention prediction without machine learning
You could look into using SkopeRules https://github.com/scikit-learn-contrib/skope-rules which trains a tree based model and picks out the best performing root-to-leaf paths as rules. It's ML under the hood but the final 'model' doesn't represent ML at all, since it would just be an if then statement.
-
D Simple Questions Thread December 20 2020
Blackbox + SHAP/LIME usually too complex/overkill for cluster analysis. https://github.com/scikit-learn-contrib/skope-rules/blob/master/notebooks/demo_clustering.ipynb gives good results with very interpretable, diverse, decision rules.
What are some alternatives?
When comparing xgboost-survival-embeddings and skope-rules you can also consider the following projects:
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
shap - A game theoretic approach to explain the output of any machine learning model.
auton-survival - Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events
scikit-survival - Survival analysis built on top of scikit-learn
Face Recognition - The world's simplest facial recognition api for Python and the command line
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
pycox - Survival analysis with PyTorch