Many thanks to Pelayo Arbués and his colleagues from Idealista, we have a great venue for our H2O.ai + Idealista joint meetup in March.
Please note, all talks will be held in English.
Pelayo and his team are hiring. If you are interested in their job openings, check this out https://www.idealista.com/empleo/ofertas/
18:30-19:00 - Drinks and networking
19:00-19:10 - Welcoming remarks by Jo-fai Chow
19:10-19:50 - First Talk: Boosting Spanish Cadastre with Machine Learning by Pelayo Arbués
19:50-20:10 - Second Talk: Using Target Encoding to Improve Predictions by Jo-fai Chow
From 20:10 - Pizzas and Drinks :)
Boosting Spanish Cadastre with Machine Learning by Pelayo Arbués
In this talk we will be presenting one of our latest projects: Catastro+. The main goal of the project is to add additional features to Spanish Cadastre by incorporating information included in Idealista ads.
Pelayo Arbués has a Ph.d. in Economics and currently works as Senior Data Scientist at idealista/data
Using Target Encoding to Improve Predictions by Jo-fai Chow
Target encoding is a feature engineering technique that is commonly used by practitioners to improve prediction accuracy. It is the process of replacing a categorical value with the mean of the target variable. Yet, target encoding is also like a double-edged sword. If it is applied without care, it could lead to overfitting and therefore do more harm than good. In order to avoid overfitting, we (H2O.ai) have implemented different target encoding strategies in our open source machine learning H2O-3. In this talk, Joe will quickly go through the basics of target encoding and then illustrate the usage with an example.
Data Science Evangelist & Community Manager at H2O.ai