Artificial intelligence algorithm model accuracy verification test method

Abstract

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence algorithm model accuracy verification test method comprising the following steps: S1, data set preparation: collecting diversified data sets to cover more scenes and conditions; s2, whether cross validation is carried out or not is judged, if yes, a cross validation method is adopted, a data set is divided into a plurality of mutually exclusive subsets, a part of the subsets are cyclically used as a test set, the rest of the subsets are used as a training set for multiple times of verification and testing so as to evaluate the performance and stability of the model, and if not, the next step is carried out; and S3, selecting a proper evaluation index, and if cross validation is not selected, selecting a proper evaluation index. According to the method, the accuracy of the artificial intelligence algorithm model can be comprehensively and systematically evaluated, developers are helped to accurately understand the performance of the model in different data sets and scenes, and the model with better performance can be screened out by comparing the performance of different models in the same test set.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence, and specifically to a method for verifying and testing the accuracy of an artificial intelligence algorithm model. Background Art

[0002] With the rapid development of artificial intelligence technology, artificial intelligence algorithm models have been widely applied in various fields. Therefore, the verification and testing of model accuracy have become a key link to ensure the stable and reliable operation of the model in practical applications.

[0003] In the prior art, there are various deficiencies in the verification and testing methods of artificial intelligence algorithm models. If the selection and processing of the data set are not reasonable enough, the generalization ability of the model in the real environment is limited; the verification and testing methods are single, and the performance and stability of the model cannot be comprehensively evaluated; the selection of evaluation indicators is not accurate enough to truly reflect the accuracy of the model; and the testing of the robustness and interpretability of the model is insufficient, resulting in possible problems in practical applications. Therefore, a method for verifying and testing the accuracy of an artificial intelligence algorithm model is proposed. Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method for verifying and testing the accuracy of an artificial intelligence algorithm model, which solves the problems that if the selection and processing of the data set are not reasonable enough, the generalization ability of the model in the real environment is limited; the verification and testing methods are single, and the performance and stability of the model cannot be comprehensively evaluated; the selection of evaluation indicators is not accurate enough to truly reflect the accuracy of the model.

[0006] (II) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for verifying and testing the accuracy of an artificial intelligence algorithm model includes the following steps:

[0009] S1: Data set preparation, collecting a diverse data set to cover more scenarios and situations;

[0010] S2: Whether to perform cross-validation. If so, the cross-validation method is adopted, the data set is divided into multiple mutually exclusive subsets, and a part of them is cyclically used as the test set, and the remaining part is used as the training set for multiple validations and tests to evaluate the performance and stability of the model. If not, proceed to the next step;

[0011] S3: Select appropriate evaluation indicators. If cross-validation is not selected, select appropriate evaluation indicators, specifically: according to the specific application scenario and requirements, select evaluation indicators to comprehensively evaluate the accuracy of the model;

[0012] S4: Model performance tuning, adjusting the model's structure, parameters, and hyperparameters to continuously optimize the model's performance and performance;

[0013] S5: Whether to conduct a robustness test. If a robustness test is selected, introduce various noises and interferences to test the performance and stability of the model in different environments to evaluate the robustness of the model. If a robustness test is not selected, perform an interpretability analysis on the model to deeply understand the model's decision-making mechanism and internal operating principle.

[0014] As a further solution of the present invention, the S1 includes a data acquisition module, and the data acquisition module acquires a reasonable and diverse data set.

[0015] Furthermore, the S1 includes a classification module. The classification module is connected to the data acquisition module, and the classification module divides the diverse data set into a training set, a validation set, and a test set to ensure that the test set is independently and identically distributed.

[0016] On the basis of the foregoing solution, the S2 includes a verification module. The verification module is connected to a division module, and the division module divides the data set into multiple mutually exclusive subsets.

[0017] Furthermore, in the S2, machine learning techniques can be used to automatically generate test cases and test data to test the artificial intelligence algorithm model.

[0018] As a further solution of the present invention, in the S4, methods such as model integration and feature selection are used to optimize the model.

[0019] On the basis of the foregoing solution, the S5 includes a test module and an analysis module. The test module tests the model, and the analysis module performs an interpretability analysis on the model.

[0020] (III) Beneficial effects

[0021] Compared with the prior art, the present invention provides a method for verifying and testing the accuracy of an artificial intelligence algorithm model, which has the following beneficial effects:

[0022] 1. In the present invention, the algorithm can be continuously optimized according to the test results to improve the performance and efficiency of the model. An accurate verification and testing method helps to expand the application scope of artificial intelligence technology and promote the rapid development of related industries.

[0023] 2. In the present invention, it is possible to comprehensively and systematically evaluate the accuracy of the artificial intelligence algorithm model, helping developers accurately understand the performance of the model in different data sets and scenarios. By comparing the performance of different models on the same test set, models with better performance can be selected.

[0024] 3. In the present invention, by introducing various noises and interferences to simulate the complex environment in the real world, the robustness of the model is tested. This helps to discover the deficiencies of the model when dealing with abnormal data or edge cases, and make targeted improvements to enhance the stability and reliability of the model.

[0025] 4. In the present invention, machine learning techniques can automatically generate test cases and test data to improve test efficiency, and the machine learning techniques can be customized and optimized according to the characteristics and requirements of the algorithm model. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic flow chart of the steps of a method for verifying the accuracy of an artificial intelligence algorithm model proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0028] Embodiment 1

[0029] Refer to Figure 1 , a method for verifying the accuracy of an artificial intelligence algorithm model, including the following steps:

[0030] S1: Dataset preparation, collect a diverse dataset covering more scenarios and situations. S1 includes a data acquisition module that acquires a reasonable and diverse dataset. S1 also includes a classification module that is connected to the data acquisition module. The classification module divides the diverse dataset into a training set, a validation set, and a test set, ensuring that the test set is independent and identically distributed and has a reasonable ratio with the training set.

[0031] S2: Whether to perform cross-validation. If yes, use the cross-validation method to divide the dataset into multiple mutually exclusive subsets, and repeatedly use a part of them as the test set and the remaining part as the training set for multiple validations and tests to evaluate the performance and stability of the model. If not, proceed to the next step. S2 includes a verification module, and the verification module is connected to a partitioning module that divides the dataset into multiple mutually exclusive subsets.

[0032] S3: Select appropriate evaluation metrics. If cross-validation is not selected, select appropriate evaluation metrics, specifically: According to the specific application scenarios and requirements, select appropriate evaluation metrics such as accuracy, precision, recall, F1-score, ROC curve, and AUC value, etc., to comprehensively evaluate the accuracy of the model. The algorithm can be continuously optimized based on the test results to improve the performance and efficiency of the model. Accurate verification and testing methods help expand the application scope of artificial intelligence technology and promote the rapid development of related industries;

[0033] S4: Model performance tuning. Adjust the structure, parameters, and hyperparameters of the model to continuously optimize the performance and performance of the model. In S4, methods such as model integration and feature selection are used to improve the accuracy and stability of the model, which can comprehensively and systematically evaluate the accuracy of the artificial intelligence algorithm model, help developers accurately understand the performance of the model under different data sets and scenarios, and by comparing the performance of different models on the same test set, a model with better performance can be selected;

[0034] S5: Whether to conduct robustness testing. If robustness testing is selected, introduce various noises and interferences to test the performance and stability of the model in different environments to evaluate the robustness of the model. If robustness testing is not selected, perform interpretability analysis on the model to deeply understand the decision-making mechanism and internal operation principle of the model. By introducing various noises and interferences to simulate complex environments in the real world, conduct robustness testing on the model, which helps to discover the deficiencies of the model when dealing with abnormal data or edge cases and make targeted improvements to improve the stability and reliability of the model.

[0035] Example 2

[0036] Refer to Figure 1 , a method for verifying and testing the accuracy of an artificial intelligence algorithm model, including the following steps:

[0037] S1: Dataset preparation. Collect diverse datasets that cover more scenarios and situations. S1 includes a data acquisition module that acquires reasonable and diverse datasets. S1 includes a classification module that is connected to the data acquisition module. The classification module divides the diverse datasets into training sets, validation sets, and test sets to ensure that the test set is independent and identically distributed and has a reasonable ratio to the training set;

[0038] S2: Whether to perform cross-validation. If so, adopt the cross-validation method, divide the dataset into multiple mutually exclusive subsets, and repeatedly use a part of them as the test set and the remaining part as the training set for multiple validations and tests to evaluate the performance and stability of the model. If not, proceed to the next step. S2 includes a validation module, and the validation module is connected to a partitioning module that divides the dataset into multiple mutually exclusive subsets. In S2, machine learning techniques can be used to automatically generate test cases and test data to test the artificial intelligence algorithm model. Machine learning techniques can automatically generate test cases and test data, improving the test efficiency. Machine learning techniques can be customized and optimized according to the characteristics and requirements of the algorithm model;

[0039] S3: Select appropriate evaluation metrics. If cross-validation is not selected, select appropriate evaluation metrics, specifically: according to the specific application scenario and requirements, select appropriate evaluation metrics such as accuracy, precision, recall, F1 value, ROC curve, and AUC value, etc., to comprehensively evaluate the accuracy of the model. The algorithm can be continuously optimized based on the test results to improve the performance and efficiency of the model. Accurate verification and testing methods help expand the application scope of artificial intelligence technology and promote the rapid development of related industries;

[0040] S4: Model performance tuning. Adjust the structure, parameters, and hyperparameters of the model to continuously optimize the performance and performance of the model. In S4, methods such as model integration and feature selection are used to improve the accuracy and stability of the model, and can comprehensively and systematically evaluate the accuracy of the artificial intelligence algorithm model, helping developers accurately understand the performance of the model under different datasets and scenarios. By comparing the performance of different models on the same test set, models with better performance can be selected;

[0041] S5: Whether to perform robustness testing. If robustness testing is selected, introduce various noises and interferences to test the performance and stability of the model in different environments to evaluate the robustness of the model. If robustness testing is not selected, perform interpretability analysis on the model to deeply understand the decision-making mechanism and internal operation principle of the model. By introducing various noises and interferences to simulate complex environments in the real world, perform robustness testing on the model, which helps to discover the deficiencies of the model when dealing with abnormal data or edge cases and make targeted improvements to improve the stability and reliability of the model. S5 includes a testing module and an analysis module. The testing module tests the model, and the analysis module performs interpretability analysis on the model.

[0042] In the description in this article, it should be noted that relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0043] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for verifying the accuracy of an artificial intelligence algorithm model, characterized in that: The following steps are involved: S1: Dataset preparation, collecting diverse datasets to cover more scenarios and situations; S2: Whether to perform cross-validation. If yes, use the cross-validation method to divide the data set into multiple mutually exclusive subsets, use part of them as the test set in a cycle, and use the remaining part as the training set for multiple validation and testing to evaluate the performance and stability of the model. If not, proceed to the next step. S3: Select appropriate evaluation indicators. If cross-validation is not selected, select appropriate evaluation indicators. Specifically, select evaluation indicators based on specific application scenarios and requirements to comprehensively evaluate the accuracy of the model. S4: Model performance tuning: adjust the model structure, parameters and hyperparameters to continuously optimize the performance and expression of the model; S5: Whether to conduct a robustness test. If you choose to conduct a robustness test, introduce various noises and interferences to test the performance and stability of the model in different environments to evaluate the robustness of the model. If you do not choose to conduct a robustness test, perform an interpretable analysis of the model to deeply understand the decision-making mechanism and internal operating principles of the model.

2. The artificial intelligence algorithm model accuracy verification test method according to claim 1 is characterized in that: The S1 includes a data acquisition module, which collects reasonable and diverse data sets.

3. The artificial intelligence algorithm model accuracy verification test method according to claim 2 is characterized in that: The S1 includes a classification module, which is connected to the data acquisition module. The classification module divides the diverse data sets into a training set, a validation set and a test set to ensure that the test set is independent and identically distributed.

4. The artificial intelligence algorithm model accuracy verification test method according to claim 1 is characterized in that: The S2 includes a verification module, the verification module is connected to a partitioning module, and the partitioning module partitions the data set into multiple mutually exclusive subsets.

5. The artificial intelligence algorithm model accuracy verification test method according to claim 1 is characterized in that: In S2, machine learning technology can be used to automatically generate test cases and test data to test the artificial intelligence algorithm model.

6. The artificial intelligence algorithm model accuracy verification test method according to claim 1 is characterized in that: In S4, the model is optimized by using the method of model integration and feature selection.

7. The artificial intelligence algorithm model accuracy verification test method according to claim 1 is characterized in that: The S5 includes a testing module and an analysis module. The testing module tests the model, and the analysis module performs interpretability analysis on the model.

Images