February reminder to check the calibration performance of clinical prediction models
Calibration: the Achilles heel of predictive analytics.
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Dec 2019 – Sep 2025Where it's discussed
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X / Twitter160 posts
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Blogs4 articles
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YouTube2 videos
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Bluesky1 posts
AI synopsis
What people are saying
Many discussions emphasize calibration's role in predictive analytics, with users noting statements like 'Calibration is the Achilles heel of predictive analytics' and sharing critiques of common tests like the Hosmer-Lemeshow test. The tone of these discussions ranges from critical to supportive, with phrases indicating urgency such as 'You want in the small generally speaking' and 'Calibration is your friend,' which underscores the necessity of addressing calibration for better predictive outcomes.
The majority of discussions indicate a consensus on the importance of calibration in predictive models, suggesting moderate to strong agreement with the article's viewpoints.
There is a high level of interest, evidenced by multiple users sharing and discussing key points from the article and expressing curiosity about calibration methods and their implications.
Engagement seems moderate; while many users reference calibration, only a subset delve into specific methodologies or detailed discussions, indicating some depth but not exhaustive exploration.
The perceived impact is considered high, as users frequently describe calibration as critical and refer to its potential consequences for clinical decision-making.
Abstract in brief
- Calibration performance in risk prediction models often gets overlooked, but it's crucial because poorly calibrated algorithms can mislead clinical decisions.
- To prevent poor calibration, it's important to balance the complexity of the model with the size of the data sample during development and validation.
- Updating algorithms and continuously evaluating their calibration can enhance their effectiveness in supporting clinical practice and improving patient counseling.
Top posts
Most liked on X and Bluesky"it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the algorithm performs in a particular setting, where predictions may go wrong" A must read to understand the importance of calibration https://t.co/C15LPsnxE5 https://t.co/N6jA1gkukm
A seemingly radical idea given the published literature, but if you're developing a prediction model, my advice would be to try & show how well it predicts -> Calibration is your friend https://t.co/JArFHziq69 Focussing only on rank based measures is not enough #statstwitter https://t.co/6sEkAhCYdJ
Evidence of questionable research practices in clinical #predictionmodel exposed by @nicolem_white @aidybarnett https://t.co/bIWwSNFmoU #BMCMedicine focus on #validation continues. Also read by @MaartenvSmeden et al https://t.co/koEmi9mSyj https://t.co/hCLwOO1gFh https://t.co/RVw5pLuNDI
“Calibration: the Achilles heel of predictive analytics” https://t.co/EZFPY669yu #calibration #conformalprediction https://t.co/9uHsbtdJej
And here we emphasize calibration for Predictive Analytics as a field: https://t.co/C4WtqetISU @BenVanCalster While clinical usefulness is an overarching metric https://t.co/4ewcfoPU2t @VickersBiostats https://t.co/kgtYNzQcqH
All mentions
The Importance of Calibration in Predictive Analytics
Calibration performance in risk prediction models often gets overlooked. Poorly calibrated algorithms can mislead clinical decisions. Balancing model complexity with data sample size is vital to enhance algorithm…
Importance of Calibration in Predictive Analytics for Medicine
Calibration performance in risk prediction models is often overlooked, yet it's crucial because poorly calibrated algorithms can mislead clinical decisions. Balancing model complexity with data sample size enhances…
https://t.co/d9QpJCJgep
7) Goodness-of-fit Hosmer-Lemeshow test The HW test is often used as a test for the calibration of prediction models. Unfortunately, it is not a good test for calibration doi.org/10.1186/s12916-019-1466-7
RT @DrAEvens: @drjgauthier Yep. #Calibration is the Achilles heel of most prognostic/predictive models.
RT @DrAEvens: @drjgauthier Yep. #Calibration is the Achilles heel of most prognostic/predictive models.
@drjgauthier Yep. #Calibration is the Achilles heel of most prognostic/predictive models.
Calibration: the Achilles heel of predictive analytics | https://t.co/9XqBUX8teH #CustomerAI #AI #AICustomerSegmentation #AIforCustomerSegmentation #ArtificialIntelligence [Video] https://t.co/WbCExbTddc
RT @doc_BLocke: @GuyattGH roughly equal wins/losses on discrimination, better calibration, and prediction+judgement > judgement alone... ye…
@GuyattGH roughly equal wins/losses on discrimination, better calibration, and prediction+judgement > judgement alone... yet the summ is "you can't presume model discrimination is better?" No one presumed that! Odd interpretation. https://t.co/RU5rCiftkK https://t.co/hHBfPCdRot
RT @DrAEvens:
@topepos @BenVanCalster I would further guess that the results will be somewhat disappointing in terms of what we can say something about the predictions after (re-)calibration, this is because recalibration can only give some guarantees on "weak calibration" https://t.co/fCa4J6LETH
RT @predict_addict: https://t.co/a5MLPEWZuH
https://t.co/a5MLPEWZuH
RT @DrAEvens: …a start. We need more rigor, including CALIBRATION: how the predicted outcome is to the actual/observed outcome in external…
RT @DrAEvens: …a start. We need more rigor, including CALIBRATION: how the predicted outcome is to the actual/observed outcome in external…
How can we calculate the slope of a calibration curve of a prognostic model? (https://t.co/nykLyqC9gx) I am trying to fit a logistic function to the curve, but results seem wrong. Any help please? @BenVanCalster @MaartenvSmeden @Richard_D_Riley @GSCollins
RT @predict_addict: Calibration the Achilles heel of predictive analytics a must read paper by @MaartenvSmeden https://t.co/I5Gv8yyT0u #…
RT @predict_addict: Calibration the Achilles heel of predictive analytics a must read paper by @MaartenvSmeden https://t.co/I5Gv8yyT0u #…
RT @predict_addict: Calibration the Achilles heel of predictive analytics a must read paper by @MaartenvSmeden https://t.co/I5Gv8yyT0u #…
RT @predict_addict: Calibration the Achilles heel of predictive analytics a must read paper by @MaartenvSmeden https://t.co/I5Gv8yyT0u #…
Calibration the Achilles heel of predictive analytics a must read paper by @MaartenvSmeden https://t.co/I5Gv8yyT0u #calibration https://t.co/B3PRt9gFHt
https://t.co/A49Cy3TAEe
RT @predict_addict: “Calibration: the Achilles heel of predictive analytics” https://t.co/EZFPY669yu #calibration #conformalprediction h…
RT @predict_addict: “Calibration: the Achilles heel of predictive analytics” https://t.co/EZFPY669yu #calibration #conformalprediction h…
RT @predict_addict: “Calibration: the Achilles heel of predictive analytics” https://t.co/EZFPY669yu #calibration #conformalprediction h…
RT @predict_addict: “Calibration: the Achilles heel of predictive analytics” https://t.co/EZFPY669yu #calibration #conformalprediction h…
“Calibration: the Achilles heel of predictive analytics” https://t.co/EZFPY669yu #calibration #conformalprediction https://t.co/9uHsbtdJej
Calibration is arguably the most important performance measure - it tests how close predicted are to observed probabilities – a key metric in the field: https://t.co/XBhaB4Dti3. For OxSATS, reported here: https://t.co/OojLaVaJjQ
Main results - in external validation, calibration good. Here are observed/expected plots. Some overestimation at higher end - relevant to v few cases. Calibration is rarely reported but key - see good paper: https://t.co/XBhaB4Dti3 https://t.co/ADPOhHpMLT
@manuel_carnero @cirujano_cardio Ahora bien, esto es la teoría y como apunta @manuel_carnero el modelo se ha validado externamente y muestra una buena discriminación y calibración, dos requisitos obligatorios y de hecho para mi la calibración es incluso más importante, por lo que... https://t.co/fJtbWxhuNW
RT @BMCMedicine: Evidence of questionable research practices in clinical #predictionmodel exposed by @nicolem_white @aidybarnett https://t…
RT @BMCMedicine: Evidence of questionable research practices in clinical #predictionmodel exposed by @nicolem_white @aidybarnett https://t…
RT @BMCMedicine: Evidence of questionable research practices in clinical #predictionmodel exposed by @nicolem_white @aidybarnett https://t…
RT @BMCMedicine: Evidence of questionable research practices in clinical #predictionmodel exposed by @nicolem_white @aidybarnett https://t…
RT @BMCMedicine: Evidence of questionable research practices in clinical #predictionmodel exposed by @nicolem_white @aidybarnett https://t…
RT @BMCMedicine: Evidence of questionable research practices in clinical #predictionmodel exposed by @nicolem_white @aidybarnett https://t…
RT @BMCMedicine: Evidence of questionable research practices in clinical #predictionmodel exposed by @nicolem_white @aidybarnett https://t…
Evidence of questionable research practices in clinical #predictionmodel exposed by @nicolem_white @aidybarnett https://t.co/bIWwSNFmoU #BMCMedicine focus on #validation continues. Also read by @MaartenvSmeden et al https://t.co/koEmi9mSyj https://t.co/hCLwOO1gFh https://t.co/RVw5pLuNDI
@DrMheissen @JClinEpi
Hosmer–Lemeshow test: often used to test for calibration in a logistic regression model. But it is not a test for calibration https://t.co/fCa4J6LETH
N.B. Calibration esp. important if use absolute % to inform decisions. Tool w/good AUC can still be systematically off-target. Examining also allows simple updating to baseline risk in new settings (as we did here). Calibration often overlooked, see
RT @MaartenvSmeden: @TomBoylesID If you are really interested here is quite some info: https://t.co/fCa4J6LETH
RT @MaartenvSmeden: @TomBoylesID If you are really interested here is quite some info: https://t.co/fCa4J6LETH
@TomBoylesID If you are really interested here is quite some info: https://t.co/fCa4J6LETH
Finally, most models are judged by their discrimination (AUC), but even models with modest discrimination might be very useful, so it is essential also to check their calibration too! https://t.co/tooQoHIwLs
…a start. We need more rigor, including CALIBRATION: how the predicted outcome is to the actual/observed outcome in external groups- aka the accuracy of absolute risk estimates. https://t.co/RXRE7w1J9w And https://t.co/c0pBhGaGwa 2/3
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
RT @Richard_D_Riley: February reminder to check the calibration performance of clinical prediction models
February reminder to check the calibration performance of clinical prediction models
@ayazhyder https://t.co/fCa4J6LETH
@FinDataScience @rasbt @marktenenholtz note 100% sure i understand your question but have a look at https://t.co/AYCKKYXVAo
But this doesn't assess calibration ... Some have called calibration the "Achilles heel of predictive analytics": https://t.co/jRwf1wFpZZ It compares predicted probabilities with observed proportions ....
@DonzelliSabrina @trumanfrancis Indeed, and we also give some arguments against it (and references) here: https://t.co/fCa4J6McJf
El otro reto fue evaluar no solo la discriminación (p.ej., areas bajo la curva ROC o similares), sino también calibración. Si quieren entender de qué trata esto y por qué es tan pero tan importante que TRIPOD y Cochrane lo solicitan, lean esto: https://t.co/7rS56dDShS (12/n)
Van Calster, G., et al.: Calibration: the achilles heel of predictive analytics. BMC Med. 17(1), 1–7 (2019) @jwilluva @OracleComms
@AGonzalezUro @trumanfrancis two good papers on calibration https://t.co/1rdVRdtouF and https://t.co/8WNopmb8kh
@trumanfrancis
@colin_fraser https://t.co/1Kn507OyBl See here. Model 2 is calibrated in the large, but Model 1 is calibrated in the small. You want in the small generally speaking.
RT @nicolem_white: A favourite paper I share with students is "Calibration: the Achilles heel of predictive analytics" from @stratosinit…
RT @nicolem_white: A favourite paper I share with students is "Calibration: the Achilles heel of predictive analytics" from @stratosinit…
https://t.co/K99pfyAkVb
RT @nicolem_white: A favourite paper I share with students is "Calibration: the Achilles heel of predictive analytics" from @stratosinit…
RT @nicolem_white: A favourite paper I share with students is "Calibration: the Achilles heel of predictive analytics" from @stratosinit…
A favourite paper I share with students is "Calibration: the Achilles heel of predictive analytics" from @stratosinit Calibration is essential when evaluating the quality of prediction models for use in clinical decision making, but is often not reported. #MyScienceMay https://t.co/wucp3DCBaA
9/ https://t.co/iWiCEtEYv9
RT @GSCollins: From a published paper Authors: "lines were almost overlapping with or close to the diagonal line". Hmm, not quite!
From a published paper Authors: "lines were almost overlapping with or close to the diagonal line". Hmm, not quite!
RT @pash22: Calibration: the Achilles heel of predictive analytics https://t.co/dlqQy6CX3U via @BenVanCalster & @GSCollins & @MaartenvSmede…
Calibration: the Achilles heel of predictive analytics https://t.co/dlqQy6CX3U via @BenVanCalster & @GSCollins & @MaartenvSmeden et al
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
RT @GSCollins: "it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the…
"it is imperative to evaluate calibration using appropriate measures & visualizations – this helps us to understand how the algorithm performs in a particular setting, where predictions may go wrong" A must read to understand the importance of calibration https://t.co/C15LPsnxE5 https://t.co/N6jA1gkukm
@GaelVaroquaux Yeah, Brier score is fine, tend to prefer to report (in addition to the calibration plot), the calibration slope & calibration-in-the-large This is what we say in TRIPOD (page W34)
RT @irfnali1: @GaelVaroquaux @Richard_D_Riley I’m also curious about the answer. This one (by @BenVanCalster, @MaartenvSmeden, @laure_wynan…
RT @irfnali1: @GaelVaroquaux @Richard_D_Riley I’m also curious about the answer. This one (by @BenVanCalster, @MaartenvSmeden, @laure_wynan…
@jschoeley Calibration is a key performance measure (& we recommend it in the TRIPOD Statement (https://t.co/dNuw3dWwmi, https://t.co/UUg4QwfFrt). You can do it by grouping & getting ave predicted risks (or fitting a loess smoother -> https://t.co/qk6paWdCmz) - not the case in this study
RT @MaartenvSmeden: Wondering what all that talk about calibration of prediction models is about? Then this paper led by @BenVanCalster mig…
RT @MaartenvSmeden: Wondering what all that talk about calibration of prediction models is about? Then this paper led by @BenVanCalster mig…
RT @MaartenvSmeden: Wondering what all that talk about calibration of prediction models is about? Then this paper led by @BenVanCalster mig…
RT @MaartenvSmeden: Wondering what all that talk about calibration of prediction models is about? Then this paper led by @BenVanCalster mig…
RT @GSCollins: A seemingly radical idea given the published literature, but if you're developing a prediction model, my advice would be to…
RT @GSCollins: A seemingly radical idea given the published literature, but if you're developing a prediction model, my advice would be to…
RT @GSCollins: A seemingly radical idea given the published literature, but if you're developing a prediction model, my advice would be to…
RT @GSCollins: A seemingly radical idea given the published literature, but if you're developing a prediction model, my advice would be to…
RT @GSCollins: A seemingly radical idea given the published literature, but if you're developing a prediction model, my advice would be to…
RT @GSCollins: A seemingly radical idea given the published literature, but if you're developing a prediction model, my advice would be to…
RT @GSCollins: A seemingly radical idea given the published literature, but if you're developing a prediction model, my advice would be to…
RT @GSCollins: A seemingly radical idea given the published literature, but if you're developing a prediction model, my advice would be to…
A seemingly radical idea given the published literature, but if you're developing a prediction model, my advice would be to try & show how well it predicts -> Calibration is your friend https://t.co/JArFHziq69 Focussing only on rank based measures is not enough #statstwitter https://t.co/6sEkAhCYdJ
RT @ESteyerberg: And here we emphasize calibration for Predictive Analytics as a field: https://t.co/C4WtqetISU @BenVanCalster While clini…
RT @ESteyerberg: And here we emphasize calibration for Predictive Analytics as a field: https://t.co/C4WtqetISU @BenVanCalster While clini…
RT @ESteyerberg: And here we emphasize calibration for Predictive Analytics as a field: https://t.co/C4WtqetISU @BenVanCalster While clini…
RT @ESteyerberg: And here we emphasize calibration for Predictive Analytics as a field: https://t.co/C4WtqetISU @BenVanCalster While clini…
And here we emphasize calibration for Predictive Analytics as a field: https://t.co/C4WtqetISU @BenVanCalster While clinical usefulness is an overarching metric https://t.co/4ewcfoPU2t @VickersBiostats https://t.co/kgtYNzQcqH
@BabakWork @GSCollins @MaartenvSmeden @ESteyerberg @TRIPODStatement https://t.co/2zFkQyMtG5
@BJSurgery @AmyLightnerMD @bplwijn @des_winter @ksoreide @MalinASund @evanscolorectal @nfmkok @robhinchliffe1 @young_bjs Hang on, if purpose == individual risk prediction, calibration far more important than discrimination (which isn't great in this case). Why only focus on AUROC? https://t.co/6407Gr2Txo
RT @pash22: @MaartenvSmeden @Argenscore @djc795 @1987Andrewk @GSCollins @rahatheart1 @mmamas1973 @VictorDayan1 @pomyers @dr_benoy_n_shah @G…
@MaartenvSmeden @Argenscore @djc795 @1987Andrewk @GSCollins @rahatheart1 @mmamas1973 @VictorDayan1 @pomyers @dr_benoy_n_shah @GilbertTangMD @DrMoritzWvB @PPibarot @DVervoort94 @gonzaeperez @drjohnm @ovidiogarciav @UniofOxford @ramspek @mpiccininni3 @escardio @UtrechtUni Maarten & @BenVanCalster et al wrote a great 2019 article "Calibration: the Achilles heel of predictive analytics" https://t.co/dlqQy6CX3U
RT @Richard_D_Riley: @AJAveritt Calibration, calibration & calibration (in the context of ML for prediction) https://t.co/8p9REgh5Ds
@AJAveritt Calibration, calibration & calibration (in the context of ML for prediction) https://t.co/8p9REgh5Ds
RT @ESteyerberg: @suchisaria @narges_razavian @IAmSamFin @NEJM @_asubbaswamy @kdpsinghlab @zittrain @zakkohane Great table! Re updating of…
RT @ESteyerberg: @suchisaria @narges_razavian @IAmSamFin @NEJM @_asubbaswamy @kdpsinghlab @zittrain @zakkohane Great table! Re updating of…
RT @ESteyerberg: @MaartenvSmeden @Richard_D_Riley @GSCollins @CarlMoons @Kym_Snell @BenVanCalster @TPA_Debray @VMTdeJong @joie_ensor @f2har…
RT @ESteyerberg: @kdpsinghlab Uninformative models cannot be clinically useful (AUC=.5); neither can a 'good' model (say AUC>.7) but a thre…
RT @ESteyerberg: @kdpsinghlab Uninformative models cannot be clinically useful (AUC=.5); neither can a 'good' model (say AUC>.7) but a thre…
RT @ESteyerberg: @kdpsinghlab Uninformative models cannot be clinically useful (AUC=.5); neither can a 'good' model (say AUC>.7) but a thre…
RT @ESteyerberg: @kdpsinghlab Uninformative models cannot be clinically useful (AUC=.5); neither can a 'good' model (say AUC>.7) but a thre…
@kdpsinghlab Uninformative models cannot be clinically useful (AUC=.5); neither can a 'good' model (say AUC>.7) but a threshold at the extreme of the risk distribution be clinically useful; and a miscalibrated model can be harmful @BenVanCalster https://t.co/C4WtqetISU https://t.co/sUJAZ8A2kT
RT @triadsou: Calibration: the Achilles heel of predictive analytics. Ben Van Calster, David J. McLernon, Maarten van Smeden, Laure Wynants…
RT @triadsou: Calibration: the Achilles heel of predictive analytics. Ben Van Calster, David J. McLernon, Maarten van Smeden, Laure Wynants…
Calibration: the Achilles heel of predictive analytics. Ben Van Calster, David J. McLernon, Maarten van Smeden, Laure Wynants & Ewout W. Steyerberg, On behalf of Topic Group ‘Evaluating diagnostic tests and prediction models’ of the STRATOS initiative. https://t.co/HhJyUIPiaj
RT @ESteyerberg: @MaartenvSmeden @Richard_D_Riley @GSCollins @CarlMoons @Kym_Snell @BenVanCalster @TPA_Debray @VMTdeJong @joie_ensor @f2har…
@MaartenvSmeden @Richard_D_Riley @GSCollins @CarlMoons @Kym_Snell @BenVanCalster @TPA_Debray @VMTdeJong @joie_ensor @f2harrell and the important issue is that 1 validation does not suffice; rather multiple validation studies should be done to give insight in heterogeneity in performance, specifically miscalibration https://t.co/IR6wFoTfpk https://t.co/C4WtqetISU https://t.co/iP9ulRLLHx
Calibration: the Achilles heel of predictive analytics https://t.co/dlqQy6CX3U via @MaartenvSmeden et al
RT @ESteyerberg: @kdpsinghlab @aminadibi Thanks for the fascinating story. I'm intrigued by the calibration of these models. If used for b…
RT @ESteyerberg: @kdpsinghlab @aminadibi Thanks for the fascinating story. I'm intrigued by the calibration of these models. If used for b…
@kdpsinghlab @aminadibi Thanks for the fascinating story. I'm intrigued by the calibration of these models. If used for benchmarking, quantifying systematic miscalibration by ICU is the objective. If used for individual decision-making, miscalibration is the Achilles heel? https://t.co/C4WtqetISU
@DrLukeOR @VickersBiostats Difficult indeed. We call calibration the Achilles heel of clinical predictive analytics. However you quantify predictions, there will be heterogeneity. I prefer to open it up maximally: risk estimation and quantification of heterogeneity between centers. https://t.co/35PxX6faF2
Calibration: the Achilles heel of predictive analytics https://t.co/zBGvDnGEy3
RT @ESteyerberg: @dt_hughes @Richard_D_Riley Congrats! Looks as a high quality study; but how can calibration-in-the-large be away from Nul…
RT @ESteyerberg: @dt_hughes @Richard_D_Riley Congrats! Looks as a high quality study; but how can calibration-in-the-large be away from Nul…
@dt_hughes @Richard_D_Riley Congrats! Looks as a high quality study; but how can calibration-in-the-large be away from Null at internal validation? Forget this performance aspect for internal, and make it the primary focus for external validation https://t.co/WTQpo5RIz0 https://t.co/2t3QIjDwIe
@tkaiser_science @krstoffr @f2harrell https://t.co/FcpOrCx528 Calibration: the Achilles heel of predictive analytics https://t.co/aUL4dd5T4S
RT @SamanthaLWilson: Great paper on predictive model tuning! I see many trying to use every variable obtained, over fitting the model. This…
Great paper on predictive model tuning! I see many trying to use every variable obtained, over fitting the model. This paper gives good suggestions to optimally calibrate predictive models. Calibration: the Achilles heel of predictive analytics. https://t.co/h6ijvgohGz
RT @GlobalPHObserv: Calibration: the Achilles heel of predictive analytics https://t.co/RY1XOSU03q
Calibration: the Achilles heel of predictive analytics https://t.co/RY1XOSU03q
RT @BMCMedicine: Just #published! A great new #opinion article from @BenVanCalster and colleagues: Calibration: the Achilles heel of pred…
RT @BMCMedicine: Just #published! A great new #opinion article from @BenVanCalster and colleagues: Calibration: the Achilles heel of pred…
Just #published! A great new #opinion article from @BenVanCalster and colleagues: Calibration: the Achilles heel of predictive analytics https://t.co/mwFuUIInSX https://t.co/WEx2gl6Kjr