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DTSTART:20001029T030000
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BEGIN:VEVENT
UID:pretalx-2023-ACPULZ@cfp.jupytercon.com
DTSTART;TZID=Europe/Paris:20230512T103000
DTEND;TZID=Europe/Paris:20230512T130000
DESCRIPTION:While the tutorial attendance is comprised in the conference p
ass\, we ask you to register for this tutorial on https://www.jupytercon.c
om/tickets as the seats available are limited.\n-- \n\nTutorial notebooks:
\n\n- https://github.com/soda-inria/survival-analysis-benchmark\n\nAccordi
ng to Wikipedia:\n\nSurvival analysis is a branch of statistics for analyz
ing the expected duration of time until one event occurs\, such as deaths
in biological organisms and failure in mechanical systems. [...]. Survival
analysis attempts to answer certain questions\, such as what is the propo
rtion of a population which will survive past a certain time? Of those tha
t survive\, at what rate will they die or fail? Can multiple causes of dea
th or failure be taken into account? How do particular circumstances or ch
aracteristics increase or decrease the probability of survival?\n\nIn this
two-part tutorial (morning and afternoon)\, we will deep dive into a prac
tical case study of predictive maintenance using tools from the scientific
Python ecosystem. Here is a tentative agenda:\n\nPart 1 (Morning)\n- What
is time-censored data and why it is a problem to train time-to-event regr
ession models.\n- Single event survival analysis with Kaplan-Meier using s
cikit-survival.\n- Competing risks modeling with Nelsonâ€“Aalen\, Aalen-Jo
hansen using lifelines.\n- Evaluation of the calibration of survival analy
sis estimators using the integrated brier score (IBS) metric.\n- Predictiv
e survival analysis modeling with Cox Proportional Hazards\, Survival Fore
sts using scikit-survival\, GradientBoostedIBS implemented from scratch wi
th scikit-learn.\n- Estimation of the cause-specific cumulative incidence
function (CIF) using our GradientBoostedIBS model.\n\nPart 2 (Afternoon)\n
- How to use a trained GradientBoostedIBS model to estimate the median sur
vival time and the probability of survival at a fixed time horizon.\n- Mea
suring the statistical association between input features and survival pro
babilities using partial dependence plot and permutation feature importanc
e.\n- Presentation of the results of a benchmark of various survival analy
sis estimators on the KKBox dataset.\n- Extracting implicit failure data f
rom operation logs using sessionization with Ibis and DuckDB.\n- Hands-on
wrap-up exercise.\n\nIt is not recommended to attend Part 2 without having
attended Part 1.\n\nTarget audience: good familiarity with machine learni
ng concepts\, with prior experience using scikit-learn (you know what cros
s-validation means and how to fit a Random Forest on a Pandas dataframe).
DTSTAMP:20241113T122854Z
LOCATION:Room 3 (Tutorial)
SUMMARY:Predictive survival analysis and competing risk modeling with sciki
t-learn\, scikit-survival\, lifelines\, Ibis\, and DuckDB (Part 1) - Guill
aume Lemaitre\, Vincent Maladiere\, Olivier Grisel
URL:https://cfp.jupytercon.com/2023/talk/ACPULZ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-2023-EHPYEN@cfp.jupytercon.com
DTSTART;TZID=Europe/Paris:20230512T140000
DTEND;TZID=Europe/Paris:20230512T160000
DESCRIPTION:This is part 2 of a two part tutorial. It is not recommended to
attend Part 2 without having attended Part 1.\n\nTutorial notebooks:\n\n-
https://github.com/soda-inria/survival-analysis-benchmark\n\n\nHere is th
e agenda of for the full session:\n\nAccording to Wikipedia:\n\nSurvival a
nalysis is a branch of statistics for analyzing the expected duration of t
ime until one event occurs\, such as deaths in biological organisms and fa
ilure in mechanical systems. [...]. Survival analysis attempts to answer c
ertain questions\, such as what is the proportion of a population which wi
ll survive past a certain time? Of those that survive\, at what rate will
they die or fail? Can multiple causes of death or failure be taken into ac
count? How do particular circumstances or characteristics increase or decr
ease the probability of survival?\n\nIn this two-part tutorial (morning an
d afternoon)\, we will deep dive into a practical case study of predictive
maintenance using tools from the scientific Python ecosystem. Here is a t
entative agenda:\n\nPart 1 (Morning)\n- What is time-censored data and why
it is a problem to train time-to-event regression models.\n- Single event
survival analysis with Kaplan-Meier using scikit-survival.\n- Competing r
isks modeling with Nelsonâ€“Aalen\, Aalen-Johansen using lifelines.\n- Eva
luation of the calibration of survival analysis estimators using the integ
rated brier score (IBS) metric.\n- Predictive survival analysis modeling w
ith Cox Proportional Hazards\, Survival Forests using scikit-survival\, Gr
adientBoostedIBS implemented from scratch with scikit-learn.\n- Estimation
of the cause-specific cumulative incidence function (CIF) using our Gradi
entBoostedIBS model.\n\nPart 2 (Afternoon)\n- How to use a trained Gradien
tBoostedIBS model to estimate the median survival time and the probability
of survival at a fixed time horizon.\n- Measuring the statistical associa
tion between input features and survival probabilities using partial depen
dence plot and permutation feature importance.\n- Presentation of the resu
lts of a benchmark of various survival analysis estimators on the KKBox da
taset.\n- Extracting implicit failure data from operation logs using sessi
onization with Ibis and DuckDB.\n- Hands-on wrap-up exercise.\n\nTarget au
dience: good familiarity with machine learning concepts\, with prior exper
ience using scikit-learn (you know what cross-validation means and how to
fit a Random Forest on a Pandas dataframe).
DTSTAMP:20241113T122854Z
LOCATION:Room 3 (Tutorial)
SUMMARY:Predictive survival analysis and competing risk modeling with sciki
t-learn\, scikit-survival\, lifelines\, Ibis\, and DuckDB (Part 2) - Guill
aume Lemaitre\, Vincent Maladiere\, Olivier Grisel
URL:https://cfp.jupytercon.com/2023/talk/EHPYEN/
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