Data Science Regression Models

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.

Machine learning is a science of artificial intelligence that can be studied by computer algorithms that are automatically improved by experience. Machine learning is a multidisciplinary field that involves computers, informatics, mathematics, statistics, neuroscience, and more.

The author of this article: Wuyuchuan &http://www.aliyun.com/zixun/aggregation/37954.html ">nbsp; The following is my experience in the past three years to do all kinds of measurement and statistical analysis of the deepest feelings, or can be helpful to everyone. Of course, it is not ABC's tutorial, nor detailed data analysis method introduction, it is only "summary" and "experience." Because what I have done is very miscellaneous, I do not learn statistics, mathematics out ...

Wen/People's Bank of China Credit Center postdoctoral Liu Nao Hai, Turbo Financial Group co-founder, chief risk officer Gu Lingyun, China Unicom Network Technology Research Institute, senior engineer Ding as an innovative technology finance company, ZestFinance since its inception, Due to its unique business philosophy, the investment community and the Internet financial sector have been the focus of attention. In 2013, the global third-party payment platform PayPal co-founder, the United States well-known investors Thiel (Peter Th ...)

Today, I will share all the questions I encountered during the interview and share how to answer them. Some of these questions are relatively normal and have a certain theoretical background, but some are very innovative.

Large data commercial revolution large data is layered, including large data 1.0, 2.0 and 3.0, here are introduced separately. Large Data 1.0 If you use a key word for large data 1.0, that is analysis, deeper, better analysis technology. As a matter of fact, members of the Hequan have just said a lot of examples and I would add some more. More interesting is the large medical data, which is more of a "quantification of the Ego", it is through a hands-off approach, some so-called medical sensors to our side, such as we wear a wrist watch, a ...

& mathematic model of how to use big data to train risk control has always been PayPal's challenge in cheating transaction detection. PayPal's training in risk control models Roughly through four stages: Decision tree: early PayPal using a simple decision tree model, mainly due to the early model training data is relatively small, the decision tree model results easy ...

Translation: Esri Lucas The first paper on the Spark framework published by Matei, from the University of California, AMP Lab, is limited to my English proficiency, so there must be a lot of mistakes in translation, please find the wrong direct contact with me, thanks. (in parentheses, the italic part is my own interpretation) Summary: MapReduce and its various variants, conducted on a commercial cluster on a large scale ...

The following small series summarizes 10 best data mining tools for everyone, which can help you analyze big data from various angles and make correct business decisions through data.

Scientific rationality and the mysterious cloak of large data--several important viewpoints on large data the basic structure of large data industry chain (data Source: Shanghai Institute of Science and Technology information) The rise of large data concepts has attracted much controversy. Some call it a "new bottle of Old wine," and others argue that the opportunity for big data is exaggerated. In fact, these are related to not really understanding the nature of large data. The development of any thing has its objective law, the big data is not "the Monkey King that jumps out in the stone", it also has its own "biological parents"--computer science and data science. ...