Statistics | PyConDE & PyData Berlin 2022

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Keynote plenary

5 Years, 10 Sprints, A scikit-learn Open Source Journey

Reshama Shaikh

Community, Science, Statistics

In this keynote, I will share highlights, challenges and lessons learned. (https://www.dataumbrella.org/sprints).

Talk pydata-machine-learning-stats

Detecting drift: how to evaluate and explore data drift in machine learning systems

Emeli Dral

Best Practice, Data Visualization, Statistics

When ML model is in production, you might encounter data and prediction drift. How exactly to detect and evaluate it? I'll share in this talk.

Talk pydata-machine-learning-stats

Honey, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations.

Florian Wilhelm

Math, Predictive Modelling, Statistics

Honey, I shrunk the target variable! Common pitfalls when transforming the target variable and how to exploit transformations.

Tutorial pydata-machine-learning-stats

Inpsect and try to interpret your scikit-learn machine-learning models

Guillaume Lemaitre

Predictive Modelling, Statistics, Transparency / Interpretability

Inspect and try to interpret your scikit-learn machine-learning models

Talk pydata-machine-learning-stats

Introduction to Uplift Modeling

Dr. Juan Orduz

Algorithms, Predictive Modelling, Statistics

In this talk we introduce uplift modelling, a method to estimate conditional average treatment effects (CATE) using machine learning estimators.

Sponsored pydata-machine-learning-stats

My forecast is better than yours! What does that even mean?

Illia Babounikau

Statistics, Time Series

Established forecast evaluation procedures often turn out to be inappropriate and biased for modern time series forecasting. I will present the number of forecast evaluations issues and resolutions based on the real use cases of demand forecasting developed within BlueYonder.

Talk pydata-natural-language-processing

Performing Content: Can NLP and Deep Learning algorithms predict reader preferences?

Sebastian Cattes

Natural Language Processing, Neural Networks / Deep Learning, Statistics

Can AI understand what drives user engagement? Join our talk "Performing Content: Can NLP and Deep Learning algorithms predict reader preferences?" to find out what NLP can bring to the editorial table.

Talk pydata-machine-learning-stats

Predictive Maintenance and Anomaly Detection for Wind Energy

Tobias Hoinka

Predictive Modelling, Statistics, Time Series

This talk will describe predictive modeling applications in wind turbine maintenance, the challenges of anomaly detection and ways to move to more automatic diagnoses by modeling past documented defects.

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