Is the law becoming less and less important?As algorithms become more commoditized, there may be fewer and fewer algorithms and more and more applications.
Have you ever thought about how we apply machine learning algorithms to problems in order to analyze, visualize, discover trends and find correlations in data? In this article, I will discuss the common steps of building a machine learning model and how to choose the right model for your data. The inspiration for this article comes from common interview questions that are asked about how to deal with data science issues and why they are chosen.
Speaking of Didi's dispatch algorithm, you may feel both mysterious and curious. From the taxi call to the driver, the Drip platform grabs the order and finally goes to the platform to send the order. Everyone’s travel experience has undergone earth-shaking changes. In response to tens of millions of calls every day, Didi's dispatch algorithm has been continuously trying to get more people to get to the car. This article will focus on how we analyze and model this problem, and what is this? Algorithm challenges, and introduce some of our commonly used dispatch algorithm, which allows us to continuously improve the user's taxi certainty.
I will tell you which machine learning model to use based on the nature of the problem, I will try to explain some concepts.
What is an algorithm? What is the difference between the artificial intelligence algorithm and the previous conventional algorithm, this article will be a detailed comparison.
How do we compare the models we have built? To compare between Model A and Model B, which is the winner, and why? Or, can we combine the two models to achieve performance optimization?