scikit-survival and pycox
These are competitors offering overlapping survival analysis functionality through different ML backends—scikit-survival uses scikit-learn's traditional algorithms while pycox specializes in neural network-based methods via PyTorch, so practitioners typically choose one based on whether they prefer classical or deep learning approaches.
About scikit-survival
sebp/scikit-survival
Survival analysis built on top of scikit-learn
This tool helps researchers and analysts predict when an event will occur, like patient recovery or machine failure, even when some subjects haven't experienced the event yet. You input data with observed event times and censored data (where the event hasn't happened or wasn't observed within the study period), and it outputs models that estimate event probabilities over time. This is for data scientists, statisticians, and researchers working with time-to-event data.
About pycox
havakv/pycox
Survival analysis with PyTorch
This tool helps researchers, clinicians, and data scientists predict the time until an event occurs, such as disease recurrence, customer churn, or machine failure. You provide data including individual characteristics and observed event times, and it outputs models that estimate the probability of the event happening over time. It's designed for anyone working with 'time-to-event' data who needs to forecast when things will happen.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work