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Results tagged with calibration
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user 1352
Calibration can refer to adjustment of measurements to agree with value of some standard; to transform classifier scores into class membership probabilities; etc. Do not use for predicting an explanatory variable from an observation of the dependent variable, for that use the tag inverse-prediction.
2
votes
Merging two model results
If the impetus behind your two models was two different subpopulations, then it sounds like the most natural course of action would be to apply the appropriate model to each sample, depending on what …
- 131k
1
vote
How to improve model fit of my predictive model?
Your analysis should always be guided by existing knowledge and theory. Don't blindly run many models and pick the "best" one, even if you cross-validate, because you may simply be overfitting to the …
- 131k
3
votes
Does Multinomial Probability Calibration Consider the Probabilities of the Non-Dominant Clas...
But I don't quite see how this preserves calibration - for the minority or the majority classes. …
- 131k
10
votes
Probability Calibration for Highly Imbalanced Binary Classification
This should show up in overestimates of the predicted probabilities, i.e., lines below the diagonal in the calibration plot, which is exactly what we see. …
- 131k
3
votes
What does calibration mean when the outcome is not categorical?
The concept of calibration is equally valid for continuous outcomes. …
- 131k
1
vote
XGBoost, Imbalanced Data and CalibratedClassifierCV
Unbalanced classes are almost certainly not a problem: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?
Do not use accuracy to evaluate a classifier:
Why is accuracy …
- 131k
1
vote
Assess calibration of a density forecast by Kolmogorov-Smirnov test on PIT of realized values
They define multiple different reasonable flavors of calibration (probabilistic, marginal and exceedance), show by examples that they are indeed logically independent and note that
Probabilistic calibration … Gneiting et al. go on to discuss other diagnostic tools for probabilistic and other flavors of calibration, and give a number of pointers to literature. …
- 131k
4
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Accepted
Why do we need separate data for probability calibration?
If you first fit your model to some data and then calibrate it using the same data, you will follow the idiosyncrasies of this dataset too closely and overfit. It makes much more sense to split your t …
- 131k