Kaggle Porto Seguro’s Safe Driver Prediction (3rd place solution) — Dmitry Altukhov

Dmitry Altukhov (utility) tells his solution of Kaggle Porto Seguro’s Safe Driver Prediction. In this competition, Kagglers were challenged to build a model that predicts the probability that a driver will initiate an auto insurance claim in the next year. From this video you will learn: - How to succeed with only two models - Greedy feature selection - Robust quality estimation with using different seeds for models and cv - Using denoting autoencoder for solving tasks Дмитрий Алтухов рассказывает про задачу предсказания использования страховки на автомобиль (Kaggle Porto Seguro’s Safe Driver Prediction). Дмитрий занял в соревновании 3 место. Из видео вы сможете узнать: - Как добиваться успеха, используя только 2 модели - Жадная фильтрация признаков - Робастная оценка качества через перебор сидов моделей и разбиений выборки - Использование denoising авкодировщика для решения задачи Yandex hosts biweekly training sessions on machine learning. These meetings offer an opportunity for the participants of data analysis contests to meet, talk, and exchange experience. Each of these events is made up of a practical session and a report. The problems are taken from Kaggle and similar platforms. The reports are given by successful participants of recent contests, who share their strategies and talk about the techniques used by their competitors. On Dec. 9, we looked at Porto Seguro’s Safe Driver Prediction challenge on Kaggle.