Interactive iterative modeling system and method

Abstract

The invention provides an interactive iterative modeling system and method. The method comprises the following steps: 1, importing data; 2, preprocessing the data; 3, selecting model parameters; 4, performing automatic configuration; 5, training a model; 6, carrying out iterative modeling; and 7, ending modeling. According to the method, the model is analyzed, the process of training the model becomes interactive, the method can be continuously based on an existing model rapid optimization process, each time of model training is no longer independent, but is changed into an iterative process,and each time of iteration and the effect of the model can be better, so that the efficiency of model training is improved, and a better model is trained in a shorter time. The method is easy to implement, high in reliability and convenient to widely popularize.

Description

technical field [0001] The invention relates to the field of robot learning, and particularly relates to a modeling system and method. Background technique [0002] At present, artificial intelligence is the focus of development in the computer field, and machine learning is the core of artificial intelligence. Machine learning mainly studies how to use algorithms to let computers simulate human learning methods, so that computers can summarize laws from data, discover new knowledge, and then use these laws or knowledge to predict future time and behavior. Compared with traditional forms of business intelligence based on expert experience, machine learning has obvious advantages. Traditional machine learning includes desktop software such as SPSS (Statistical Product and Solutions), SAS (Statistical Analysis System), or uses programming languages ​​such as R and Python to develop specific programs to process and mine specific data. However, these traditional machine learni...

AI Technical Summary

Problems solved by technology

However, these traditional machine learning methods have high requirements on the user's application ability or require the user to have good programming ability

Therefore, in today's era of popularization of big data and artificial intelligence, it is difficult for this traditional machine learning method to be quickly promoted and popularized, and it also has great shortcomings in terms of development efficiency and deployment reuse.

Images