How To Choose Features Machine Learning

"Csdn Live Report" December 2014 12-14th, sponsored by the China Computer Society (CCF), CCF large data expert committee contractor, the Chinese Academy of Sciences and CSDN jointly co-organized to promote large data research, application and industrial development as the main theme of the 2014 China Data Technology Conference (big Data Marvell Conference 2014,BDTC 2014) and the second session of the CCF Grand Symposium was opened at Crowne Plaza Hotel, New Yunnan, Beijing. 2014 China large data Technology ...

Machine learning is a combination of art and science. No machine learning algorithm can solve all the problems. There are several factors that can influence your decision to choose a machine learning algorithm.

Open source machine learning tools also allow you to migrate learning, which means you can solve machine learning problems by applying other aspects of knowledge.

The scarcity of machine learning talent and the company's commitment to automating machine learning and completely eliminating the need for ML expertise are often on the headlines of the media.

Machine learning sounds like a wonderful concept, and it does, but there are some processes in machine learning that are not so automated. In fact, when designing a solution, many times manual operations are required.

With the development and popularity of artificial intelligence technology, Python has surpassed many other programming languages and has become one of the most popular and most commonly used programming languages in the field of machine learning.

Developing new machine learning algorithms and describing how they work and why work is a science is often not necessary when developing a learning system.

Recently, Airbnb machine learning infrastructure has been improved, making the cost of deploying new machine learning models into production environments much lower. For example, our ML Infra team built a common feature library that allows users to apply more high-quality, filtered, reusable features to their models.

The performance of different machine learning algorithms depends on the size and structure of the data. Therefore, unless we use traditional trial and error experiments, we have no clear way to prove that a choice is right.

There are many articles on machine learning algorithms that detail the related algorithms, it is still very difficult to make the most appropriate choices.