Gradient Boosting Regression: From Kaggle to business applications

Y-DATA Meetup #6
Gradient Boosting Regression: From Kaggle to business applications

Hosted by JoyTunes
Talks are in English

Intro:

Most data science meetups tend to focus on neural networks and their latest advances. Y-DATA meetup series is not an exception to this rule. However, at least once a year we dive into gradient boosting. This family of algorithms is still very popular, handy and efficient when dealing with classification and regression tasks. This is the case in both ML competitions and real business applications. In the upcoming meetup we will talk specifically about gradient boosting regression. In the first talk, we'll discuss custom loss functions and target transformation on an example from Kaggle competition including implementation comparison among most popular GBT libraries (CatBoost, LightGBM, XGBoost). In the second talk, we'll present some creative ways to use it in real-world business tasks.

More info about Y-DATA is here: bit.ly/ydata-website
Previous meetups vidoes are here: bit.ly/youtube-ydata

Agenda:

18:00 - 18:30 Registration, Mingling, Snacks & Beer

18:30 - 19:15 Talk 1: Gradient boosting regression and MAE -
Dmitry Dryomov, DS consultant and Kaggle Master

19:15 - 19:30 Break

19:30 - 20:15 Predicting user acquisition campaigns results with gradient boosting regression - Asaf Adi, Data Scientist at JoyTunes

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Talk Details:

Talk #1:
Title:
Gradient boosting regression and MAE (mean absolute error)

Abstract:
Many models need MAE to be optimized. Kaggle competitions with MAE metrics are both an important source of ideas on a topic and a way to benchmark these ideas.
In this talk, we will go through median averaging, custom objectives and target transformation. We will compare their performance in the past Kaggle competition and review implementations available in CatBoost, LightGBM, and XGBoost

Bio:
Dmitry Dryomov is a DS consultant and Kaggle Master (🥇x 3) with highest competition rank 98.
In the past Dmitry worked for Yandex and Pontis (now a part of Amdocs). Former organizer of ML training meetup (2013-2014) and CDS TA meetup (in 2017) on Kaggle competitions.
He holds first and second degrees in Applied Mathematics. He is also a graduate of Yandex School of Data Analysis (class of '13)

Talk #2:
Title:
Predicting user acquisition campaigns results with gradient boosting regression

Abstract:
Having an early prediction of the effectiveness of User Acquisition (UA) campaigns can have a dramatic effect on optimizing acquisition budgets, cutting response times and reducing manual analysis time of UA managers. This effect is even more important when the UA team works under aggressive growth targets and performance constraints.
In this talk we will discuss the challenges we faced when implementing a Gradient Boosting Regression algorithm to predict campaign performance 90 days ahead.

Bio:
Asaf Adi is a product Data Scientist at JoyTunes, responsible for all user acquisition data aspects.
Assaf has experience in the performance Marketing/Gaming data-sphere, past projects included user LTV prediction.
Has a B.A in economics and taught himself to code, ML and DL in the past years.