autoprognosis VS auton-survival

Compare autoprognosis vs auton-survival and see what are their differences.

autoprognosis

A system for automating the design of predictive modeling pipelines tailored for clinical prognosis. (by vanderschaarlab)

auton-survival

Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events (by autonlab)
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autoprognosis auton-survival
2 1
89 293
- 1.0%
5.0 4.4
7 months ago 29 days ago
Python Python
Apache License 2.0 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.

autoprognosis

Posts with mentions or reviews of autoprognosis. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-25.

auton-survival

Posts with mentions or reviews of auton-survival. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing autoprognosis and auton-survival you can also consider the following projects:

hyperimpute - A framework for prototyping and benchmarking imputation methods

keras - Deep Learning for humans [Moved to: https://github.com/keras-team/keras]

scikit-survival - Survival analysis built on top of scikit-learn

xgboost-survival-embeddings - Improving XGBoost survival analysis with embeddings and debiased estimators

pycox - Survival analysis with PyTorch

causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.

pgmpy - Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.