Model generation method, apparatus, computer device, and storage medium

Abstract

The invention provides a model generation method, device, a computer device and a storage medium. The method comprises: receiving original training data input by a user, specifying a variable type ofthe original training data, and specifying a training model algorithm; Invoking a pre-stored data preprocessing method corresponding to the variable type; using the data preprocessing method to preprocess the original training data to obtain the processed training data; Invoking the training model algorithm; machine learning is performed on the training data using the processed training model algorithm to generate a machine learning model. A method for pre-store data corresponding to different parameter types Given a variety of optional initial training models and algorithms, we only need to call appropriate data preprocessing methods to process the data and call appropriate initial training models or algorithms to generate machine learning models, which greatly reduces the workload of engineers and improves the efficiency of acquiring machine learning models.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a model generation method, device, computer equipment and storage medium. Background technique [0002] The machine learning process generally includes a data preprocessing process and a model training process. At present, in the process of machine learning, both the data preprocessing process and the model training process require engineers to implement it by writing code. However, in many cases, the data preprocessing methods and model training methods used in the machine learning process have a high degree of similarity. If engineers are required to write code every time, it will inevitably bring a large workload to the engineers, resulting in repetitive work, wasting time and reducing the efficiency of product development. In addition, for engineers or non-engineers with insufficient professionalism, due to professional restrictions and other issues, data p...

AI Technical Summary

Problems solved by technology

If engineers are required to write code every time, it will inevitably bring a large workload to engineers, resulting in repetitive work, waste of time and reduce the efficiency of product development

In addition, for engineers or non-engineers with insufficient professionalism, due to professional restrictions and other issues, data preprocessing cannot be performed. In the case that the model training process cannot correctly select the model algorithm, modeling is very difficult, and the efficiency of modeling very low

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