loft-br/xgboost-survival-embeddings
Improving XGBoost survival analysis with embeddings and debiased estimators
This project helps data scientists and analysts make more robust predictions about how long something will last or survive, such as customer retention, equipment lifespan, or credit default. It takes historical data on events and durations and outputs predictions of survival probabilities over time, complete with confidence intervals. This allows for more reliable decision-making in various business and scientific contexts.
343 stars. No commits in the last 6 months.
Use this if you need to predict the timing of future events with high accuracy and understand the uncertainty around those predictions.
Not ideal if your primary need is for simple 'yes/no' or 'A/B/C' classifications rather than time-to-event predictions.
Stars
343
Forks
53
Language
Python
License
Apache-2.0
Category
Last pushed
Oct 03, 2024
Commits (30d)
0
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