Comments (7)
@ablaom currently MLJ models can give deterministic & probabilistic predictions (GLM predicts an entire distribution).
If methods for conformal predictive inference become more developed, can users automatically combine their favorite (appropriate) deterministic predictor w/ their favorite predictive interval method, to make probabilistic predictions?
For example:
currently model = LGBMRegressor()
gives deterministic predictions
would a user be able to create a new model composing a deterministic model LGBMRegressor
w/ their favorite (appropriate) predictive interval method e.g. conformal(type=naive)
to create a new model w/ probabilistic predictions?
Note: at some point it would be great to compare these w/ predictions/Prediction Intervals from NGBoost.py etc
from conformalprediction.jl.
For example:
currently model = LGBMRegressor() gives deterministic predictions
would a user be able to create a new model composing a deterministic model LGBMRegressor w/ their favorite (appropriate) predictive interval method e.g. conformal(type=naive) to create a new model w/ probabilistic predictions?
Yes, I guess that's the design I am suggesting. So, similar to the way BinaryThresholdPredictor
wraps any probabilistic predictor and makes it deterministic.
from conformalprediction.jl.
Hi @ablaom 👋🏽 Thanks very much for this suggestion and sorry for the delayed response (been battling Covid this week while also trying to finish my JuliaCon proceedings submission 😅 ). I will implement this first thing once I turn back to working on this package some time next week 👍🏽
from conformalprediction.jl.
Implemented in #10, but will keep this open for now, because I still want to iron out a few things (some related questions here).
from conformalprediction.jl.
#18 should now be strictly in line with MLJ @ablaom, so I will close this. Will still need to figure out how to handle downstream tasks like evaluate
in the future.
from conformalprediction.jl.
Did you not need new abstract model subtype(s) at MLJModelInterface? For set-predictions (we already have Interval
).
from conformalprediction.jl.
Yup, you're right. Have done that now in #20
from conformalprediction.jl.
Related Issues (20)
- Conformal Training examples HOT 2
- Support for thresholding predictive distributions as explained in Section 2.4 of the tutorial HOT 2
- Conformal Bayes through 'add-one-in' importance sampling
- .vscode folder HOT 1
- Add Aqua.jl
- Add parallelizer field to all models
- Adaptive Inductive Classification broken? HOT 2
- Move to adjusted quantile HOT 1
- Class-Conditional CP with many classes
- Treat data as artifacts
- JuliaCon pres
- Add format check to CI
- Add support for RAPS
- [Refactor] Separate module for TS
- Revisit sample correction
- Move plot methods to TaijaPlotting.jl
- Add TaijaPlotting to docs env HOT 1
- Add support for 1.6 HOT 1
- readme Quick Tour notebook: "Could not fetch rendered notebook or notebook source." HOT 5
- Conformal Training
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from conformalprediction.jl.