Data processing method in learning modeling

Abstract

The invention provides a data processing method in learning modeling, and belongs to the technical field of Python machine learning modeling, and aims to convert a regression problem with a large amount of required information into a multi-classification problem with a small amount of required information by utilizing log transformation and equal-width standardization on the basis of a power law relationship and aiming at a y variable conforming to long tail distribution, so that subsequent modeling work can be carried out smoothly.

Description

technical field [0001] The invention relates to the technical field of Python machine learning modeling solutions, in particular to a data processing method in learning modeling. Background technique [0002] The power law comes from the analysis of the frequency of English words in the 1920s. There are very few words that are really commonly used, and many words are not often used. Linguists found that the frequency of word use and its use priority are a constant power inverse relationship. To be precise, in simple terms, the power law is two popular laws, one is the "long tail" theory, there are only a few large portals that many people pay attention to, but there is also a long tail, which is small websites, small company. The long-tail theory is a popular explanation of the power law. Another popular explanation is the Matthew effect, where the poor get poorer and the rich get richer. [0003] Discretization is a commonly used technique in programming, which can effe...

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Problems solved by technology

[0005] The invention proposes a data processing method in learning modeling, which solves the problem of discretizing the dependent variable conforming to the long-tail distribution based on the power-law relationship in the modeling process of the machine learning algorithm.

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