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https://github.com/glemaitre/talks
https://github.com/glemaitre/talks
Last synced: 27 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/glemaitre/talks
- Owner: glemaitre
- Created: 2017-06-11T10:03:51.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-12-05T11:22:09.000Z (about 1 month ago)
- Last Synced: 2024-12-05T12:25:21.630Z (about 1 month ago)
- Language: Jupyter Notebook
- Size: 40.5 MB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# TRACES winter school 2024
## Introduction to machine learning in Python
*Tutorial*: This tutorial introduces how to use scikit-learn to craft predictive models
using machine learning. It covers the basics of machine learning: the evaluation,
give insights about linear models, tree-based models, discussed about hyperparameter
tuning and finally goes a bit into confidence intervals prediction.
Tutorials repository
Static course
# Sample space podcast 2024
## Imbalanced-learn: regrets and onwards
*Abstract*: Imbalanced-learn is one of the most popular `scikit-learn` projects
out there. It has support for resampling techniques which historically have
always been used for imbalanced classification use-cases. However, now that we
are a few years down the line, it may be time to start rethinking the library.
As it turns out, other techniques may be preferable.
# Practical AI podcast 2024
## scikit-learn & data science you own
*Abstract*: We are at GenAI saturation, so let’s talk about `scikit-learn`, a long time
favorite for data scientists building classifiers, time series analyzers, dimensionality
reducers, and more! `Scikit-learn` is deployed across industry and driving a significant
portion of the “AI” that is actually in production. :probabl is a new kind of company
that is stewarding this project along with a variety of other open source projects. Yann
Lechelle and Guillaume Lemaitre share some of the vision behind the company and talk
about the future of `scikit-learn`!
# ENGIE 2024
## scikit-learn: community insights & latest features
*Abstract*: Insights regarding the `scikit-learn` community and some new features
available in the latest versions.
# PyData Paris 2024
## An update on the latest scikit-learn features
*Abstract*: In this talk, we provide an update on the latest `scikit-learn`
features that have been implemented in versions 1.4 and 1.5. We will
particularly discuss the following features:
- the metadata routing API allowing to pass metadata around estimators;
- the `TunedThresholdClassifierCV` allowing to tuned operational decision through custom
metric;
- better support for categorical features and missing values;
- interoperability of array and dataframe.
Slides
Videos
Tutorials repository
# EuroSciPy 2024
## Probabilistic classification and cost-sensitive learning with scikit-learn
*Tutorial*: Data scientists are repeatedly told that it is absolutely critical to align
their model training methodology with a specific business objective. While being a
rather good advice, it usually falls short on details on how to achieve this in
practice.
This hands-on tutorial aims to introduce helpful theoretical concepts and concrete
software tools to help them bridge this gap. This method will be illustrated on a worked
practical use case: optimizing the operations of a fraud detection system for a payment
processing platform.
More specifically, we will introduce the concepts of calibrated probabilistic
classifiers, how to evaluate them and fix common causes of mis-calibration. In a second
part, we will explore how to turn probabilistic classifiers into optimal business
decision makers.
# DeepLabCut AI Residency 2024
## scikit-learn: An OSS community-driven development
*Abstract*: This talk provides insights regarding the `scikit-learn` community
and the development of the library.
# Sacl-AI 2024
## Introduction to machine learning in Python
*Tutorial*: This tutorial provides an introduction to machine learning in Python,
notably using `scikit-learn`.
# DataTalksClub podcast 2024
## Insights regarding the scikit-learn project
*Abstract*: This podcast discusses some insights regarding the `scikit-learn` project
and `imbalanced-learn` project.
# PyCon Italia 2024
## A Retrieval Augmented Generation system to query the scikit-learn documentation
*Abstract*: Rubber ducks are used for many years to help Pythonistas in their everyday
quest. At scikit-learn, we’ve elevated ducky to another level: come and meet the
scikit-learn Ragger Duck, a RAG system designed to answer all your scikit-learn
questions – at least as effectively as a duck can.
Slides
Blog post
Tutorials repository
# PyConDE & PyData Berlin 2024
## A Retrieval Augmented Generation system to query the scikit-learn documentation
*Abstract*: The scikit-learn website currently employs an "exact" search engine based on
the Sphinx Python package, but it has limitations: it cannot handle spelling mistakes
and queries based on natural language. To address these constraints, we experimented
with using large language models (LLMs) and opted for a retrieval augmented generation
(RAG) system due to resource constraints.
This talk introduces our experimental RAG system for querying scikit-learn
documentation. We focus on an open-source software stack and open-weight models. The
talk presents the different stages of the RAG pipeline. We provide documentation
scraping strategies that we designed based on numpydoc and sphinx-gallery, which are
used to build vector indices for the lexical and semantic searches. We compare our RAG
approach with an LLM-only approach to demonstrate the advantage of providing context.
The source code for this experiment is available on GitHub:
https://github.com/glemaitre/sklearn-ragger-duck.
Finally, we discuss the gains and challenges of integrating such a system into an
open-source project, including hosting and cost considerations, comparing it with
alternative approaches.
Slides
Blog post
Videos
Tutorials repository
# CDiscount 2024
## Get the best from your scikit-learn classifier
*Abstract*: When operating a classifier in a production setting (i.e. predictive phase),
practitioners are interested in potentially two different outputs: a "hard" decision
used to leverage a business decision or/and a "soft" decision to get a confidence score
linked to each potential decision (e.g. usually related to class probabilities).
Scikit-learn does not provide any flexibility to go from "soft" to "hard" predictions:
it uses a cut-off point at a confidence score of 0.5 (or 0 when using decision_function)
to get class labels. However, optimizing a classifier to get a confidence score close to
the true probabilities (i.e. a calibrated classifier) does not guarantee to obtain
accurate "hard" predictions using this heuristic. Reversely, training a classifier for
an optimum "hard" prediction accuracy (with the cut-off constraint at 0.5) does not
guarantee obtaining a calibrated classifier.
In this talk, we will present a new scikit-learn meta-estimator allowing us to get the
best of the two worlds: a calibrated classifier providing optimum "hard" predictions.
This meta-estimator will land in a future version of scikit-learn:
https://github.com/scikit-learn/scikit-learn/pull/26120.
We will provide some insights regarding the way to obtain accurate probabilities and
predictions and also illustrate how to use in practice this model on different use
cases: cost-sensitive problems and imbalanced classification problems.
# PyData Paris Meetup 2024
## Get the best from your scikit-learn classifier
*Abstract*: When operating a classifier in a production setting (i.e. predictive phase),
practitioners are interested in potentially two different outputs: a "hard" decision
used to leverage a business decision or/and a "soft" decision to get a confidence score
linked to each potential decision (e.g. usually related to class probabilities).
Scikit-learn does not provide any flexibility to go from "soft" to "hard" predictions:
it uses a cut-off point at a confidence score of 0.5 (or 0 when using decision_function)
to get class labels. However, optimizing a classifier to get a confidence score close to
the true probabilities (i.e. a calibrated classifier) does not guarantee to obtain
accurate "hard" predictions using this heuristic. Reversely, training a classifier for
an optimum "hard" prediction accuracy (with the cut-off constraint at 0.5) does not
guarantee obtaining a calibrated classifier.
In this talk, we will present a new scikit-learn meta-estimator allowing us to get the
best of the two worlds: a calibrated classifier providing optimum "hard" predictions.
This meta-estimator will land in a future version of scikit-learn:
https://github.com/scikit-learn/scikit-learn/pull/26120.
We will provide some insights regarding the way to obtain accurate probabilities and
predictions and also illustrate how to use in practice this model on different use
cases: cost-sensitive problems and imbalanced classification problems.
# PyData Global 2023
## Get the best from your scikit-learn classifier
*Abstract*: When operating a classifier in a production setting (i.e. predictive phase),
practitioners are interested in potentially two different outputs: a "hard" decision
used to leverage a business decision or/and a "soft" decision to get a confidence score
linked to each potential decision (e.g. usually related to class probabilities).
Scikit-learn does not provide any flexibility to go from "soft" to "hard" predictions:
it uses a cut-off point at a confidence score of 0.5 (or 0 when using decision_function)
to get class labels. However, optimizing a classifier to get a confidence score close to
the true probabilities (i.e. a calibrated classifier) does not guarantee to obtain
accurate "hard" predictions using this heuristic. Reversely, training a classifier for
an optimum "hard" prediction accuracy (with the cut-off constraint at 0.5) does not
guarantee obtaining a calibrated classifier.
In this talk, we will present a new scikit-learn meta-estimator allowing us to get the
best of the two worlds: a calibrated classifier providing optimum "hard" predictions.
This meta-estimator will land in a future version of scikit-learn:
https://github.com/scikit-learn/scikit-learn/pull/26120.
We will provide some insights regarding the way to obtain accurate probabilities and
predictions and also illustrate how to use in practice this model on different use
cases: cost-sensitive problems and imbalanced classification problems.
# EuroSciPy 2023
## Get the best from your scikit-learn classifier
*Abstract*: When operating a classifier in a production setting (i.e. predictive phase),
practitioners are interested in potentially two different outputs: a "hard" decision
used to leverage a business decision or/and a "soft" decision to get a confidence score
linked to each potential decision (e.g. usually related to class probabilities).
Scikit-learn does not provide any flexibility to go from "soft" to "hard" predictions:
it uses a cut-off point at a confidence score of 0.5 (or 0 when using decision_function)
to get class labels. However, optimizing a classifier to get a confidence score close to
the true probabilities (i.e. a calibrated classifier) does not guarantee to obtain
accurate "hard" predictions using this heuristic. Reversely, training a classifier for
an optimum "hard" prediction accuracy (with the cut-off constraint at 0.5) does not
guarantee obtaining a calibrated classifier.
In this talk, we will present a new scikit-learn meta-estimator allowing us to get the
best of the two worlds: a calibrated classifier providing optimum "hard" predictions.
This meta-estimator will land in a future version of scikit-learn:
https://github.com/scikit-learn/scikit-learn/pull/26120.
We will provide some insights regarding the way to obtain accurate probabilities and
predictions and also illustrate how to use in practice this model on different use
cases: cost-sensitive problems and imbalanced classification problems.
# PyConDE & PyData Berlin 2022
## Inspect an try to interpret your `scikit-learn` machine-learning models
*Abstract*: This tutorial is subdivided into three parts. First, we focus on
the family of linear models and present the common pitfalls to be aware of when
interpreting the coefficients of such models. Then, we look at a larger range
of models (e.g. gradient-boosting) and put into practice available inspection
techniques developed in `scikit-learn` to inspect such models. Finally, we
present other tools to interpret models (i.e. `shap`), not currently available
in `scikit-learn`, but widely used in practice.
Slides
Videos
Tutorials repository
# PyLadies Paris 2022
## Inspecting your predictive model in Python
*Abstract*: This presentation intends to present the available tools allowing
to inspect your predictive model in Python. We will first quickly present
what we mean by predictive model and what it implies when one wants to explain
the decision of such a model. We will provide a quick taxonomy of the current
methods intending to explain predictive model. Finally, we will give an
overview of the available tools in `scikit-learn` and `shap`.
# Euler Hermes 2019
## Learning from imbalanced datasets: state of the art
*Abstract*: This presentation gives an overview of the state of the art of
predictive modelling with imbalanced datasets.
# Euroscipy 2019
## Rapid Analytics & Model Prototyping (RAMP)
*Abstract*: We will give an overview of the RAMP framework, which provides a
platform to organize reproducible and transparent data challenges. RAMP
workflow is a python package used to define and formalize the data science
problem to be solved. It can be used as a standalone package and allows a user
to prototype different solutions. In addition to RAMP workflow, a set of
packages have been developed allowing to share and collaborate around the
developer solutions. Therefore, RAMP database provides a database structure to
store the solutions of different users and the performance of these solutions.
RAMP engine is the package to run the user solutions (possibly on the cloud)
and populate the database. Finally, RAMP frontend is the web frontend where
users can upload their solutions and which shows the leaderboard of the
challenge. The project is open-source and can be deployed on any local server.
The framework has been used at the Paris-Saclay Center for Data Science for
setting up and solving about twenty scientific problems, for organizing
collaborative data challenges, for organizing scientific sub-communities around
these events, and for training novice data scientists.
Slides
RAMP board
RAMP workflow
## Introduction to `scikit-learn`: from model fitting to model interpretation
*Abstract*: Our introduction to scikit-learn will be subdivided into 2 parts.
We will give a general introduction to scikit-learn presenting basic concepts
around cross-validation, pipeline estimator, and hyperparameter search. Then,
we will focus on model interpretation presenting the challenges and the
available tools to understand a trained machine-learning model: partial
independence plot, features importance, LIME, shapley values, etc.
# Euroscipy 2018
## Imbalanced-learn: A scikit-learn-contrib to tackle learning from imbalanced data set
*Abstract*: The curse of imbalanced data set refers to data sets in which the
number of samples in one class is less than in others. This issue is often
encountered in real world data sets such as medical imaging applications
(e.g. cancer detection), fraud detection, etc. In such particular condition,
machine learning algorithms learn sub-optimal models which will generally favor
the class having the largest number of samples. In this talk, we review the
different available strategy to learn a statistical model under those specific
condition. Then, we will present `imbalanced-learn` package and the new
features which will be released in the new version 0.4.
# CDS Pitching Day 2017
## RAMP on predicting autism from resting-state functional MRI and anatomical MRI
*Abstract*: This talk will present the ongoing preparation of a RAMP aiming at
distinguishing subjects with Autism Spectrum Disorder (ASD) from typical
control subjects. This analysis will use the Autism Brain Imaging Data Exchange
(ABIDE I & II) database and data from Robert Debre Hospital based on R-fMRI and
anatomical MRI. We will particularly focus on presenting the problematic, the
typical pipeline answering this problem, and the current status of this RAMP.
This work is in collaboration with the Pasteur Institute (Neuroanatomy group of
the Unit of Human Genetics and Cognitive Functions).
# Euroscipy 2017
## Leverage knowledge from under-represented classes in machine learning: imbalanced-learn release 0.3.0
*Abstract*: The curse of imbalanced data set refers to data sets in which the
number of samples in one class is less than in others. This issue is often
encountered in real world data sets such as medical imaging applications
(e.g. cancer detection), fraud detection, etc. In such particular condition,
machine learning algorithms learn sub-optimal models which will generally favor
the class having the largest number of samples. In this talks, we present the
new feature which are available in the release 0.3.0.
# PyParis 2017
## Leverage knowledge from under-represented classes in machine learning: an introduction to imbalanced-learn
*Abstract*: The curse of imbalanced data set refers to data sets in which the
number of samples in one class is less than in others. This issue is often
encountered in real world data sets such as medical imaging applications
(e.g. cancer detection), fraud detection, etc. In such particular condition,
machine learning algorithms learn sub-optimal models which will generally favor
the class having the largest number of samples. In this talk, we will present
the imbalanced-learn package which implement some of the state-of-the-art
algorithms, tackling the class imbalance problem.