Глава
Fantasy football meets machine learning: the dynamic game case and a note on strategy
P. 128-129.
Рассматриваются некоторые сценарии использования конкурентных структур данных, показывающие повышение производительности при увеличении времени работы одного потока, которому остальные потоки делегируют свои задачи. Данный подход получил название flat-combining (FC) [1]. Представлены несколько разработанных стратегий синхронизации, описаны их преимущества и область применения.
This research is based on Kaggle1 competition for Caterpillar Inc. Caterpillar Inc. sells a variety of construction and mining equipment. Each machine relies on a complex set of tubes. Tubes can vary across a number of dimensions, material, bends, length and other parameters. Caterpillar Inc. relies on a variety of suppliers to manufacture these tube assemblies, each having their own unique pricing model. The challenge is to predict supplier's tube price based on tube parameters. Caterpillar Inc. reviles novel field to apply machine learning technique. In this paper I reveal a good approach to solve this task. This solution ranked in a top 10% among more than 1300 contestants. Ranking was based on RMSLE and the solution achieved 0.218223. I found useful to ensemble various random forests [1] predictions with xgboost [2] library.