Method and system for integrated machine learning convenient for data analysis personnel to use

Abstract

The invention relates to the technical field of machine learning, and especially relate to a method and a system for integrated machine learning convenient for data analysis personnel to use. The method comprises the following steps: (1) data exploring; (2) data cleaning; (3) feature extraction; (4) feature selection; (5) sampling; (6) model training; (7) model optimization; (8) model combination;(9) model interpretability; (10) nature language processing. The system comprises a data processing module, a feature processing module, a model processing module, and a nature language processing module. The method and the system provide a unified algorithm modeling process for machine learning engineers, students, teachers, and machine learning fans, so that the machine learning engineers, students, teachers, and machine learning fans complete a modeling process by 20% efforts, and concentrate 80% efforts on understanding of business and model application, to deeply understand business andpreferably realize requirements of business personnel on models.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to an integrated machine learning method and system convenient for data analysts to use. Background technique [0002] There are two industry pain points in big data modeling. First, data cleaning tasks are heavy and mechanical, including heavy data cleaning tasks and time constraints. It is difficult to reuse the data cleaning function codes of different projects. Second, the modeling process is cumbersome, including the "modeling-evaluation-parameter tuning-evaluation" process, which is time-consuming and cumbersome. The model fusion process is complex and tedious. Traditional machine learning developers only spend 20% of their time understanding the business, but spend 80% of their energy on data cleaning, model tuning, and other modeling tasks. The final model is often quite different from business expectations. There is an urgent need to launch a product in the mark...

AI Technical Summary

Problems solved by technology

It is difficult to reuse the data cleaning function code of different projects

Second, the modeling process is cumbersome, including the time-consuming and cumbersome process of "modeling-evaluation-parameter tuning-evaluation"

The model fusion process is complex and cumbersome

Traditional machine learning developers only spend 20% of their time understanding the business, but spend 80% of their energy on data cleaning, model tuning, and other modeling tasks. The final model often differs greatly from business expectations

Images