JP6899475B1 - Machine learning model accuracy analysis system, machine learning model accuracy analysis method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a machine learning model accuracy analysis system capable of identifying a factor in which the prediction accuracy of a predicted value output by a black-boxed machine learning model deteriorates. A white box model creation unit 23 included in an analysis system 2 uses input data used for creating a black box model 15 as an explanatory variable in accordance with the creation of the black box model 15, and outputs the black box model 15. The white box model 24 is created with the predicted value to be used as the objective variable. When it is determined that the accuracy of the predicted value output from the black box model 15 has changed during the determination period in which the accuracy of the predicted value of the black box model 15 is determined, the accuracy deterioration factor identification unit 25 sets the determination period. The accumulated input data is input to the white box model 24 to identify the cause of the change in the accuracy of the predicted value. [Selection diagram] Fig. 1

Description

The present invention relates to a machine learning model accuracy analysis system, a machine learning model accuracy analysis method and a program.

In recent years, the development of artificial intelligence technology (hereinafter referred to as AI (Artificial Intelligence)) has been remarkable. By performing machine learning by the AI program itself, it is possible to find the characteristics of the input data acquired during a predetermined learning period and create a machine learning model that outputs the predicted value.

If the predicted value output by the machine learning model accurately represents the measured value actually measured by the control system that is actually operated, predict the operation of the control system based on the predicted value of the machine learning model. Is possible. However, the control system operated for a long period of time changes from the state at the time of learning due to various factors. For this reason, the predicted value output by the machine learning model may gradually deviate from the measured value, and the prediction accuracy may change.

Generally, the structure of a machine learning model is black-boxed, and it is difficult to grasp the processing contents of the machine learning model from the outside. Therefore, it was difficult to explain that the prediction accuracy of the machine learning model changed. In the following description, the black boxed machine learning model is referred to as a "black box model".

For such problems, a technique called XAI (Explainable AI) is being developed that can explain the basis for predicting the predicted value by AI (for example, a machine learning model). As a conventional XAI, a technique for identifying a factor for which the output data output from the machine learning model is calculated by using the learning data used when constructing the machine learning model has been provided.

For example, as a conventional technique for expressing XAI, the techniques disclosed in Patent Documents 1 and 2 are known. In Patent Document 1, "a deep learning prediction means that executes prediction processing using a deep learning model based on data stored in a database and a prediction result by the deep learning prediction means are set as objective variables and are stored in the database. Multiple regression analysis is performed using the above data as explanatory variables, and variables for explaining the prediction results of the deep learning model are determined based on the results of the multiple regression analysis. "

Further, Patent Document 2 states, "It supports an analyst to determine a model (explanatory variable data) of a model execution device and appropriately reconstruct (remodel) the model by machine learning by displaying useful information on a screen. ".

International Publication No. 2018/1427353 Patent No. 6625183

By the way, in the technique disclosed in Patent Document 1, it is not possible to know from the outside how the variables for explaining the prediction result of the deep learning model are used in the deep learning model.

Further, even in the technique disclosed in Patent Document 2, the reconstruction of the calculation model is merely promoted, and the cause of the deterioration of the accuracy of the calculation model cannot be grasped. As described above, the conventional technique is insufficient to explain the factors that change the prediction accuracy of the black-boxed machine learning model.

The present invention has been made in view of such a situation, and an object of the present invention is to identify a factor in which the prediction accuracy of the predicted value output by the black-boxed machine learning model has changed.

The machine learning model accuracy analysis system according to the present invention has a learning period set in advance from an input database in which input data including the results of processing performed by the control device on the controlled device used in the control system is accumulated. It is a black boxed machine learning model that is trained based on the input data read in, and is matched with the creation of a black boxed machine learning model that can output the processing result as a predicted value from the input data. With the white box model creation unit that creates a white box model using the input data used to create the black boxed machine learning model as an explanatory variable and the predicted value output by the black boxed machine learning model as the objective variable. , If it is determined that the accuracy of the predicted value output from the blackboxed machine learning model has changed during the judgment period in which the accuracy of the predicted value of the blackboxed machine learning model is determined, the judgment period It is provided with a factor identification unit for inputting the input data accumulated in the white box model and identifying the factor for which the accuracy of the predicted value has changed.

According to the present invention, it is possible to input the input data accumulated during the determination period into the white box model and identify the cause of the change in the accuracy of the predicted value output from the black boxed machine learning model. Become.
Issues, configurations and effects other than those described above will be clarified by the following description of the embodiments.

It is a block diagram which shows the whole structure example of the control system and the analysis system which concerns on 1st Embodiment of this invention. It is a figure which shows the example of the flow of various data used in the control system and the analysis system which concerns on 1st Embodiment of this invention. It is a figure which shows the structural example of the decision tree model which concerns on 1st Embodiment of this invention. It is a figure which shows the structural example of the clustering model which concerns on 1st Embodiment of this invention. It is a block diagram which shows the hardware configuration example of the computer which concerns on 1st Embodiment of this invention. It is a flowchart which shows the example of the processing of the whole analysis system which concerns on 1st Embodiment of this invention. It is a flowchart which shows the example of the process which the white box model creation part which concerns on 1st Embodiment of this invention creates a white box model. FIG. 5 is a flowchart showing an example of a process in which the accuracy deterioration factor specifying unit according to the first embodiment of the present invention identifies the accuracy deterioration factor of the predicted value of the black box model. It is a flowchart which shows the example of the process which the remodeling method proposal part which concerns on 1st Embodiment of this invention proposes the remodeling method of a black box model. It is a figure which shows the example of the proposal screen of the remodeling method which concerns on 1st Embodiment of this invention. It is a flowchart which shows the example of the process which the remodeling method proposal part which concerns on 2nd Embodiment of this invention proposes the remodeling method of a black box model. It is a figure which shows the example of the proposal screen of the remodeling method which concerns on the 2nd Embodiment of this invention.

Hereinafter, embodiments for carrying out the present invention will be described with reference to the accompanying drawings. In the present specification and the drawings, components having substantially the same function or configuration are designated by the same reference numerals, and redundant description will be omitted.

[First Embodiment]
<Analysis system configuration example>
First, a machine learning model accuracy analysis system that analyzes the accuracy of a black-boxed machine learning model according to the first embodiment of the present invention will be described.
FIG. 1 is a block diagram showing a configuration example of the control system 1 and the analysis system 2.

FIG. 1 shows a control system 1 used by a customer and an analysis system 2 (machine learning model accuracy analysis system) used by a service provider who provides a predetermined service to a customer to analyze the operation of the control system 1. An example) is shown. The control system 1 includes, for example, a measuring device 11, a device 12, an input database 13, a black box model creating device 14, a black box model 15, and an accuracy data storage database 16.

(Control system)
First, an example of the internal configuration of the control system 1 will be described.
The measuring device 11 (an example of a control device) measures the temperature of a device 12 (an example of a controlled device) used by a customer. The digital or analog measured value obtained by measuring the device 12 by the measuring device 11 is stored as input data in the input database 13 (described as “input DB” in the figure).

The input database 13 is composed of, for example, a large-capacity non-volatile storage 67 (see FIG. 5 described later) such as an HDD (Hard Disk Drive), and stores a large amount of input data. In the input database 13, the result of the processing performed by the measuring device 11 on the device 12 used in the control system 1 is stored as input data. This input data includes an actually measured value obtained by the measuring device 11 actually measuring the device 12. The process performed by the measuring device 11 on the device 12 includes a process of measuring the workpiece processed by the device 12.

The input data stored in the input database 13 is used when the black box model creation device 14 learns to build the black box model 15. The input data used during learning is also called "learning data". The input data stored in the input database 13 is also used when the verification unit 22 evaluates the predicted value of the black box model 15 after the operation of the control system 1 is started.

The black box model creation device 14 reads the input data of the learning period from the input database 13 at the time of learning. As described above, the input data input to the black box model creation device 14 are all actually measured values. Then, the black box model creating device 14 learns the characteristics of the input data based on the input data read in the preset learning period, and creates the black box model 15. An example of the black box model 15 constructed during training is shown on the left side of the figure. This black box model 15 is an example of a black boxed machine learning model constructed by using the input data of the measured values input from the input database 13 as explanatory variables and objective variables, and the internal configuration is open to the public other than the customer. It has not been.

When the control system 1 is put into operation, the black box model 15 is put into operation. The customer who manages the measuring device 11 and the device 12 monitors the state of the device 12 and predicts the operation of the device 12 based on the predicted value output by the black box model 15. The black box model 15 that operates during the operation of the control system 1 is arranged on the right side in the drawing, and it is shown that the prediction process is performed using the black box model 15.

Input data, which is an actually measured value, read from the input database 13 is input to the black box model 15 as an explanatory variable. Then, the black box model 15 outputs the predicted value with respect to the actually measured value as an objective variable, and stores it as accuracy data in the accuracy data storage database 16 (described as “precision data storage DB” in the figure).

The accuracy data storage database 16 stores the input data of the measured values read from the input database 13 as the objective variable. Further, in the accuracy data storage database 16, the predicted value output from the black box model 15 described above is stored as an objective variable. That is, the accuracy data accumulated in the accuracy data storage database 16 includes an actually measured value acquired by measuring the device 12 by the measuring device 11 and a predicted value predicted by the black box model 15.

(Analysis system)
Next, an example of the internal configuration of the analysis system 2 will be described.
The analysis system 2 analyzes the prediction accuracy of the predicted value of the black box model 15. The black box model 15 is an example of a black boxed machine learning model. The internal processing of the black box model 15 cannot be grasped by the service provider. This analysis system 2 is used by a service provider that identifies a factor that deteriorates the prediction accuracy of the black box model 15 (hereinafter, referred to as “accuracy deterioration factor”). The analysis system 2 can be retrofitted to the existing control system 1. The analysis system 2 includes an accuracy determination unit 21, a verification unit 22, and a proposal learning data extraction unit 27.

The accuracy determination unit 21 determines the accuracy of the predicted value output from the black box model 15 in advance by the service provider during the accuracy determination period (also referred to as “at the time of measurement”) in which the accuracy of the predicted value of the black box model 15 is determined. When the value changes from the set threshold value (accuracy threshold value), it is determined that the accuracy of the predicted value has deteriorated. Here, the "accuracy of the predicted value" means the ratio of the predicted value output from the black box model 15 to the actually measured value. Then, when the accuracy of the predicted value determined at the time of measurement is lower than the threshold value with respect to the accuracy of the predicted value determined at the time of learning, it is said that the accuracy of the predicted value has deteriorated. For example, when the threshold value is set to 70% and the accuracy of the predicted value with respect to the measured value at the time of measurement is 65%, the predicted value becomes lower than the threshold value, so that the accuracy determination unit 21 determines the accuracy of the predicted value. Judge that it has deteriorated. The threshold value set to 70% is determined based on the accuracy of the predicted value with respect to the measured value at the time of learning.

Therefore, the accuracy determination unit 21 receives the accuracy data read from the accuracy data storage database 16 at the timing when the black box model 15 is created as an input, and determines whether or not the prediction accuracy of the predicted value with respect to the actually measured value is good or bad. For example, if the accuracy of the predicted value in the determination period is equal to the accuracy of the predicted value in the learning period, there is no problem in the operation of the black box model 15. On the other hand, if the accuracy of the predicted value in the determination period deviates from the accuracy of the predicted value in the learning period, the control system 1 cannot use the data output by the black box model 15 as the predicted value. Therefore, the accuracy determination unit 21 outputs the determination result of the prediction accuracy to the verification unit 22.

(Identification of factors that deteriorate accuracy)
Here, an example of internal configuration and an example of operation of the verification unit 22 will be described.
When the verification unit 22 verifies the predicted value output by the black box model 15 and identifies the cause of deterioration in accuracy, the verification unit 22 proposes to the customer a remodeling method of the black box model 15. The verification unit 22 is used to evaluate the performance of the existing black box model 15 created by the customer and identify the cause of deterioration in accuracy. Therefore, similarly to the analysis system 2, it is possible to retrofit only the verification unit 22 to the existing control system 1. The verification unit 22 includes a white box model creation unit 23, a white box model 24, an accuracy deterioration factor identification unit 25, and a remodeling method proposal unit 26.

In the white box model creation unit 23, the service provider grasps the internal processing in accordance with the creation of the black box model 15 which can output the result of the processing by the measuring device 11 as a predicted value from the input data read from the input database 13. Create a possible white box model 24. In the white box model 24 creation process, first, the input data including the predicted value predicted by the black box model 15 arranged on the left side in the figure and the actually measured value read from the input database 13 is the white box model creating unit 23. Is entered in. Then, the white box model creation unit 23 can create the white box model 24 using the input data used for creating the black box model 15 as an explanatory variable and the predicted value output by the black box model 15 as an objective variable. .. As will be described later, the white box model creation unit 23 also operates during remodeling (re-learning) of the black box model 15.

The white box model 24 imitates the processing of the black box model 15. The service provider can confirm the branching of the process, the content of the judgment, and the like through the white box model 24, and can grasp the internal structure of the white box model 24. On the left side of the figure, an example of the white box model 24 created together with the black box model 15 is shown. The white box model 24 may be stored, for example, in the non-volatile storage 67 (see FIG. 5 described later) included in the verification unit 22.

Then, when the verification unit 22 identifies the cause of deterioration in the accuracy of the predicted value, the white box model 24 arranged on the right side in the drawing operates. The input data of the actually measured values read from the input database 13 is input to the white box model 24 as an explanatory variable and an objective variable. The reason why the explanatory variables and the objective variables of the measured values are input to the white box model 24 is that the accuracy deterioration factor identification unit 25 determines the correct branch and the incorrect branch in the branch in the white box model 24. is there. By determining the correctness of the branch, the accuracy deterioration factor specifying unit 25 can grasp the wrong part of the black box model 15, that is, identify the accuracy deterioration factor.

Therefore, the accuracy deterioration factor identification unit 25 (an example of the factor identification unit) determines the accuracy of the predicted value when the accuracy determination result input from the accuracy determination unit 21 is a result indicating that the accuracy of the predicted value has deteriorated. In order to identify the cause of the deterioration, the white box model 24 arranged on the right side of FIG. 1 is operated. At this time, when the accuracy deterioration factor identification unit 25 determines that the accuracy of the predicted value output from the black box model 15 has changed during the determination period in which the accuracy of the predicted value of the black box model 15 is determined, The input data accumulated in the input database 13 during the determination period is input to the white box model 24 to identify the cause of the change in the accuracy of the predicted value.

At this time, the accuracy deterioration factor identification unit 25 performs a process of identifying the accuracy deterioration factor based on the predicted value output from the white box model 24 and the internal structure of the white box model 24 itself. Then, the accuracy deterioration factor identification unit 25 outputs the specified accuracy deterioration factor to the remodeling method proposal unit 26.

The remodeling method proposal unit 26 (an example of the proposal unit) remodels the black box model 15 (also referred to as “re-learning”) when the factor whose accuracy of the predicted value has changed is identified by the accuracy deterioration factor identification unit 25. Propose the method of. In the proposed remodeling method, for example, the black box model is used by using the input data after the period when the accuracy of the predicted value of the black box model 15 deteriorates (data after February 2020 shown in FIG. 10 to be described later). Specific methods such as proposing 15 re-learning are included.

The remodeling method proposal unit 26 proposes a remodeling method to the service provider. Therefore, the remodeling method proposing unit 26 can improve the accuracy of the predicted value by remodeling the black box model 15 with a part of the processing specified as the accuracy deterioration factor from the white box model 24 or by any remodeling method. Information such as whether or not it is possible is output to the display device 65 (see FIG. 5 described later).

The remodeling method proposed by the remodeling method proposal unit 26 is provided not only to the service provider but also to the customer. Then, in the analysis system 2, the remodeling method proposal unit 26 outputs a data extraction instruction based on the remodeling method of the black box model 15 to the proposal learning data extraction unit 27. Therefore, with the consent of the customer, the analysis system 2 extracts the data necessary for remodeling the black box model 15 from the input database 13 again, and provides the extracted data to the control system 1. The control system 1 remodels the black box model 15 using the provided data.

(Remodeling)
Next, the process of remodeling the black box model 15 will be described.
The proposal learning data extraction unit 27 (an example of the extraction unit) at the upper left of FIG. 1 starts a process of extracting input data from the input database 13 based on the instruction output from the remodeling method proposal unit 26. The instruction output from the remodeling method proposal unit 26 is, for example, an execution instruction of the remodeling method proposed by the remodeling method proposal unit 26 to the service provider and obtained approval. When the remodeling method proposal unit 26 proposes, for example, a plurality of remodeling methods, the service provider selects one remodeling method. Then, the proposed learning data extraction unit 27 is started by the execution instruction of the remodeling method selected by the service provider. Then, the proposal learning data extraction unit 27 uses the input data extracted from the input database 13 based on the remodeling method instructed by the remodeling method proposal unit 26 into the black box model creation device 14 for creating the black box model 15. Output.

The input data extracted from the input database 13 by the proposed learning data extraction unit 27 is used as the proposed learning data required for remodeling the black box model 15. For example, from the input database 13, the input data accumulated after the time when the accuracy determination unit 21 determines that the accuracy of the predicted value of the black box model 15 has deteriorated is extracted as the proposed learning data. The proposed learning data extracted by the proposed learning data extraction unit 27 is output to the black box model creating device 14.

When the remodeling method proposed by the remodeling method proposal unit 26 is modified (for example, the input data extraction period is changed), the proposed learning data extraction unit 27 remodeling method according to the modified method. The proposal unit 26 outputs an instruction for data extraction based on the remodeling method. Even in this case, the proposal learning data extraction unit 27 can extract the input data (proposal learning data) from the input database 13 according to the instruction of data extraction.

The black box model creating device 14 recreates the black box model 15 by using the proposed learning data input from the proposed learning data extraction unit 27 as the relearning data. The re-creation of the black box model 15 is a process of re-creating the black box model 15 by using the proposed learning data of the actually measured values input as the re-learning data from the proposed learning data extraction unit 27 as the explanatory variables and the objective variables. is there. After this process, the control system 1 performs a process of outputting a predicted value with respect to the actually measured value by using the recreated black box model 15.

When the black box model 15 is recreated, the white box model creation unit 23 recreates the white box model 24. At this time, the proposed learning data input to the black box model creation device 14 at the time of recreating the black box model 15 is input to the white box model creation unit 23 as relearning data. Then, the white box model creation unit 23 recreates the white box model 24 by using the re-learning data of the measured values input from the proposed learning data extraction unit 27 as the explanatory variables and the objective variables. Such accuracy determination of the predicted value of the black box model 15, identification of the cause of deterioration of accuracy, and re-creation of the black box model 15 and the white box model 24 are repeatedly performed.

<Data flow>
Next, the flow of data used in each system will be described.
FIG. 2 is a diagram showing an example of various data flows used in the control system 1 and the analysis system 2. In FIG. 2, in order to pay attention to the data flow, the description of the black box model creation device 14, the accuracy determination unit 21, and the white box model creation unit 23 is omitted.

A large amount of input data is stored in the input database 13 shown in FIG. As described above, the black box model creation device 14 creates the black box model 15 by using the input data as an input. The white box model creation unit 23 creates the white box model 24 using the input data and the data output by the black box model 15.

After the start of operation of the control system 1, the black box model 15 takes input data, which is an actually measured value after the start of operation, as an input and outputs a predicted value. However, when the operation period of the control system 1 becomes long, the prediction accuracy of the predicted value changes from the time when the black box model 15 is created. Therefore, when the prediction accuracy calculated by the accuracy determination unit 21 shown in FIG. 1 deteriorates from a predetermined threshold value, a process for identifying the cause of the deterioration in the prediction accuracy of the black box model 15 is performed. For example, when the prediction accuracy calculated by the accuracy determination unit 21 changes from 80% to less than 70% (change of threshold value 10% or more), it is determined that the prediction accuracy has deteriorated.

However, the service provider cannot know the content of the process from the outside of the black box model 15. Therefore, the white box model creation unit 23 creates a white box model 24 that visualizes the processing of the black box model 15. At this time, the white box model creation unit 23 creates the white box model 24 using the input data (measured value) read from the input database 13 as an explanatory variable and the output data (predicted value) of the black box model 15 as an objective variable. .. In the figure, it is shown that, for example, the decision tree model 24a and the clustering model 24b were created as the white box model 24.

<Example of white box model>
Here, a configuration example of the decision tree model 24a and the clustering model 24b used as an example of the white box model 24 will be described with reference to FIGS. 3 and 4.

(Decision tree model)
FIG. 3 is a diagram showing a configuration example of the decision tree model 24a. The decision tree model is a method of classifying events using a tree structure consisting of a root at the top, a node connected to the root, and a leaf that is a node without children. Here, it is assumed that the white box model 24 is represented by the decision tree model 24a. The decision tree model 24a is composed of a decision tree that branches an event that appears in the device 12 at a predetermined occurrence rate under a predetermined condition, and the event is specified by the decision tree. The decision tree model 24a is composed of a route 31, a node 33, and leaves 32, 34, 35.

(At the time of learning)
First, the configuration of the decision tree model 24a at the time of learning when the black box model creation device 14 created the black box model 15 will be described. In the decision tree model 24a, an event having an occurrence rate of 15% (for example, a moisture value of the device 12 to be measured, a defect of the device 12, etc.) is classified in the route 31. Here, it is assumed that 1000 or more events were used to create the black box model 15 and the white box model 24 at the time of learning. Then, at the time of learning, as shown in the decision tree model display unit W1a of FIG. 10, which will be described later, the occurrence rate of the event represented by the node 33 of the decision tree model 24a is set against the branching condition in which the plate thickness is 20 or more. It is assumed that the occurrence rate of the event represented by the leaf 34, which is 30% and the heating temperature is less than 200 ° C., is 50%. Here, the occurrence rate of the event calculated at the time of learning is written in parentheses in the vicinity of the node 33 and the leaves 34 and 35.

(At the time of measurement)
Next, when the prediction accuracy of the black box model 15 changes (deteriorates) after the start of operation of the control system 1, that is, at the time of measurement of the device 12 by the measuring device 11, what kind of configuration the decision tree model 24a changes to. Explain what to do. It is the same as at the time of learning that the decision tree model 24a classifies the events having an occurrence rate of 15% on the route 31. Then, it is assumed that 500 events were used to create the white box model 24 at the time of measurement.

However, at the time of measurement, it is assumed that the occurrence rate of the event represented by the node 33 is reduced from 30% to 25%, and the occurrence rate of the event represented by the leaf 34 is further reduced from 50% to 30%. That is, the occurrence rate of the event represented by the leaf 34 was 50% at the time of learning, but decreased by 20% at the time of measurement to 30%, so that the accuracy at the branch 36 is remarkably deteriorated. In this way, the accuracy deterioration factor identification unit 25 inputs the input data accumulated in the determination period into the decision tree model 24a, and calculates the event occurrence rate for each branch of the decision tree.

Then, the accuracy deterioration factor identification unit 25 determines when the amount of change between the event occurrence rate calculated during the learning period and the event occurrence rate calculated during the determination period becomes larger than the threshold value. Identify the branching condition as a factor that changes the accuracy of the predicted value. The identified accuracy deterioration factor is displayed on the display device 65 by the remodeling method proposing unit 26. As described above, when the decision tree model 24a is used as the white box model 24, the service provider can easily grasp the factors that deteriorate the accuracy of the predicted value of the black box model 15.

(Clustering model)
FIG. 4 is a diagram showing a configuration example of the clustering model 24b. The white box model 24 uses a clustering method to generate a clustering model 24b divided into subsets having the same characteristics for explanatory variables (measured values). Normally, a plurality of explanatory variables (measured values) are used, but here, two explanatory variables will be used for easy explanation. For example, it is composed of three clusters, clusters 41, 42, and 43, as a subset having the same characteristics consisting of explanatory variables of "plate thickness" and "heating temperature". Therefore, the white box model 24 is represented by a clustering model 24b that classifies the events appearing in the device 12 into clusters 41, 42, and 43 having the same characteristics.

(At the time of learning)
First, the configuration of the clustering model 24b at the time of learning when the black box model creation device 14 created the black box model 15 will be described. In the clustering model 24b, it is assumed that the occurrence rate of the event represented by the cluster 43 having a plate thickness of 20 or more and a heating temperature of less than 200 ° C. is 50%. Here, the occurrence rate of the event calculated at the time of learning is also written in parentheses in the vicinity of the clusters 41, 42, and 43.

(At the time of measurement)
Next, when the prediction accuracy of the black box model 15 changes (deteriorates) after the start of operation of the control system 1, that is, at the time of measurement of the device 12 by the measuring device 11, what kind of configuration the clustering model 24b changes to. Explain.

At the time of measurement, it is assumed that the occurrence rate of the event represented by the cluster 43 has decreased from 50% to 30%. That is, the occurrence rate of the event represented by the cluster 43 was 50% at the time of learning, but decreased by 20% at the time of measurement to 30%, so that the accuracy in the cluster 43 is remarkably deteriorated. In this way, the accuracy deterioration factor identification unit 25 inputs the input data accumulated during the determination period to the clustering model 24b, and calculates the event occurrence rate for each cluster.

The accuracy deterioration factor specifying section 25, the incidence of an event that is calculated in the learning period, the amount of change in the incidence of an event that is calculated in the decision period, when it becomes greater than the threshold value, the amount of change is Identify the characteristics of the enlarged cluster as a factor that changed the accuracy of the predicted value. The accuracy deterioration factor identified by the accuracy deterioration factor identification unit 25 is displayed on the display device 65 by the remodeling method proposal unit 26. As described above, when the clustering model 24b is used as the white box model 24, the service provider can easily grasp the cause of deterioration in the accuracy of the predicted value of the black box model 15.

Here, the accuracy deterioration factor identification unit 25 calculates the distance 44 between the cluster 41, which is the closest to the cluster 43, and the center of the cluster 43, for which the amount of change has increased. This is to extract the characteristics of the cluster by knowing which explanatory variable values are significantly different from the closest cluster. The distance 44 is premised on the Euclidean distance, but may be any other distance (Mahalanobis distance, Chebyshev distance, Minkowski distance, etc.). Further, the distance component for each explanatory variable for the distance 44 is extracted. The distance component of the distance 44 is extracted, for example, "10" for the plate thickness and "5" for the heating temperature. At this time, since the distance component of the "plate thickness" is larger than the distance component of the "heating temperature", it is specified that the "plate thickness" is a factor that changes the accuracy of the predicted value.

(Processing of analysis system)
According to the above explanation, the accuracy of the predicted value output from the black box model 15 at the time of measurement deteriorated because the branching condition shown in FIG. 3 or the distance component in the distance between clusters shown in FIG. 4 was the largest. It was shown that the variable was the factor. Therefore, the accuracy deterioration factor identification unit 25 can identify the accuracy deterioration factor by using the white box model 24.

<Computer hardware configuration example>
Next, the hardware configuration of the computer 60 composed of the respective systems of the control system 1 and the analysis system 2 will be described.
FIG. 5 is a block diagram showing a hardware configuration example of the computer 60. The computer 60 is an example of hardware used as a computer that realizes each function of the control system 1 and the analysis system 2.

The computer 60 includes a CPU (Central Processing Unit) 61, a ROM (Read Only Memory) 62, a RAM (Random Access Memory) 63, a display device 65, an input device 66, a non-volatile storage 67, and a network connected to the bus 64, respectively. It has an interface 68.

The CPU 61 reads the program code of the software that realizes each function according to the present embodiment from the ROM 62, loads it into the RAM 63, and executes the program code. Variables and parameters generated during the arithmetic processing of the CPU 61 are temporarily written in the RAM 63, and these variables and parameters are appropriately read by the CPU 61. However, an MPU (Micro Processing Unit) may be used instead of the CPU 61.

The display device 65 is, for example, a liquid crystal display monitor, and displays the result of processing performed by the computer 60 or the like to a user who uses each system. For example, a keyboard, a mouse, or the like is used as the input device 66, and the user can perform predetermined operation inputs and instructions.

As the non-volatile storage 67, for example, an HDD, SSD (Solid State Drive), flexible disk, optical disk, magneto-optical disk, CD-ROM, CD-R, magnetic tape, non-volatile memory, or the like is used. In this non-volatile storage 67, in addition to the OS (Operating System) and various parameters, a program for operating the computer 60 is recorded. The ROM 62 and the non-volatile storage 67 record programs, data, and the like necessary for the CPU 61 to operate, and as an example of a computer-readable non-transient storage medium that stores a program executed by the computer 60. Used.

For the network interface 68, for example, a NIC (Network Interface Card) or the like is used, and various data can be transmitted and received between the devices via a LAN (Local Area Network) connected to the terminal of the NIC, a dedicated line, or the like. It is possible. For example, the control system 1 and the analysis system 2 can send and receive data via the network interface 68.

<Processing of control system and analysis system>
Next, the processing of the control system 1 and the analysis system 2 as a whole will be described with reference to FIGS. 6 to 9.
FIG. 6 is a flowchart showing an example of processing of the entire control system 1 and analysis system 2. The process shown by the broken line in FIG. 6 represents the process performed by the control system 1, and the process shown by the solid line in FIG. 6 represents the process performed by the analysis system 2 (an example of the machine learning model accuracy analysis method).

First, the black box model creation device 14 creates the black box model 15 using the learning data read from the input database 13 (S1).

Next, the white box model creation unit 23 creates the white box model 24 using the learning data read from the input database 13 (S2). Here, the process of creating the white box model 24 in step S2 will be described with reference to FIG. 7.

<White box model creation process>
FIG. 7 is a flowchart showing an example of the creation process of the white box model 24.
First, the white box model creation unit 23 prepares a predicted value output from the black box model 15 into which the training data is input (S11).

Next, the white box model creation unit 23 uses the predicted value output from the black box model 15 as the objective variable and the same training data as the one input to the black box model 15 as the explanatory variable, and sets the decision tree model 24a (white). The box model 24) is created (S12). In the following description, it is assumed that the white box model 24 is represented by the decision tree model 24a shown in FIG.

Next, the white box model creation unit 23 calculates the accuracy of each branch of the decision tree model 24a (white box model 24) (S13). Then, the white box model creation unit 23 returns the process to FIG.

The explanation of FIG. 6 will be continued again.
After the white box model 24 is created in step S2 of FIG. 6, the operation of the control system 1 is started. The input database 13 stores the input data of the measured values used in the control system 1, and the accuracy data storage database 16 stores the input data read from the input database 13 after the start of operation of the control system 1. Accumulate.

Next, the accuracy determination unit 21 monitors the accuracy of the predicted value output from the black box model 15 into which the input data is input (S3). At this time, the accuracy determination unit 21 calculates the accuracy of the predicted value by comparing the actually measured value stored in the accuracy data storage database 16 with the predicted value output from the black box model 15.

Next, the accuracy determination unit 21 determines whether or not the accuracy of the predicted value of the black box model 15, that is, the accuracy of the predicted value output from the black box model 15 is poor (S4). When the accuracy determination unit 21 determines that the accuracy of the predicted value of the black box model 15 is not bad (NO in S4), the accuracy determination unit 21 returns to step S3 again and continues monitoring.

On the other hand, when the accuracy determination unit 21 determines that the accuracy of the predicted value of the black box model 15 is poor (YES in S4), the accuracy deterioration factor identification unit 25 performs a process of identifying the accuracy deterioration factor (S5). Here, with reference to FIG. 8, a process for identifying the accuracy deterioration factor shown in step S5, which is performed by the accuracy deterioration factor identification unit 25, will be described.

<Identification of factors that deteriorate accuracy>
FIG. 8 is a flowchart showing an example of a process in which the analysis system 2 identifies a factor that deteriorates the accuracy of the predicted value of the black box model 15.

First, when the accuracy determination unit 21 determines that the accuracy of the black box model 15 applied to the new input data has deteriorated, the accuracy deterioration factor identification unit 25 is input to the black box model 15. The same input data as is read from the input database 13, and this input data is applied to the determination tree model 24a (white box model 24) (S21).

Next, the accuracy deterioration factor identification unit 25 calculates the accuracy of each branch of the decision tree model 24a with respect to the current input data applied in step S21 (S22).

Next, the accuracy deterioration factor identification unit 25 determines the accuracy of each branch of the decision tree model 24a when the training data is input to the white box model 24 (described as “training data” in the figure) and the input data this time. Is compared with the time when is input (indicated as "input data time" in the figure). Then, the accuracy deterioration factor specifying unit 25 identifies a portion having a large difference in accuracy (for example, the branch 36 in FIG. 3) as a branch of the accuracy deterioration factor (S23). After step S23, the accuracy deterioration factor identification unit 25 returns the process to FIG.

After step S5, the remodeling method proposal unit 26 proposes a remodeling method for the black box model 15 (S6). Here, with reference to FIG. 9, a process of proposing a remodeling method of the black box model 15 shown in step S6 will be described.

<Proposal processing of remodeling method>
FIG. 9 is a flowchart showing an example of a process in which the remodeling method proposal unit 26 proposes a remodeling method for the black box model 15.

First, the remodeling method proposal unit 26 pays attention to the branch in which the deterioration factor is identified with respect to the new input data used by the accuracy deterioration factor identification unit 25 to identify the accuracy deterioration factor, and the period of this branch. Calculate the accuracy of each. This input data is read from the input database 13 by the accuracy deterioration factor identification unit 25 during the determination period. Then, the remodeling method proposal unit 26 extracts the change point of the accuracy indicating the time when the accuracy of the branch in which the deterioration factor is specified changes (S31).

Next, the remodeling method proposal unit 26 proposes to remodel the black box model 15 using the input data that can be acquired after the extracted accuracy change point as new learning data (S32). As this proposal, for example, a process of displaying a proposal screen of the remodeling method shown in FIG. 10 to be described later is performed.

In the proposal processing of the remodeling method, the proposal screen W1 that can be confirmed by the service provider is displayed as shown in FIG. 10 described later. In this proposal screen W1, how is the occurrence rate of branching of the decision tree model that appears when the service provider inputs the input data of the judgment period to the white box model 24 already created by machine learning? It is used to see if it changes. Further, on the proposal screen W1, a remodeling method for the service provider and the customer is proposed. After step S32, the process returns to FIG. 6, and when the customer approves the proposal of the remodeling method, the process returns to step S1 of FIG. 6 and the black box model 15 is recreated.

In the process of recreating the black box model 15, the change point of the branching accuracy in which the deterioration factor is specified by the accuracy deterioration factor identification unit 25 extracted from the input database 13 by the proposal learning data extraction unit 27 in step S1 of FIG. Subsequent input data is used as re-learning data. Then, the black box model creation device 14 recreates the black box model 15. After that, at the time of measurement, the recreated black box model 15 is used.

<Proposal screen for remodeling method>
FIG. 10 is a diagram showing a display example of the proposal screen W1 of the remodeling method. The proposal screen W1 is displayed, for example, on the display device 65 shown in FIG.

The proposal screen W1 includes decision tree model display units W1a and W1b, and a graph display unit W1c. The remodeling method proposal unit 26 includes a determination period including the output result of the white box model 24 into which the input data used during the training of the black box model 15 is input and the time when the accuracy of the predicted value of the black box model 15 changes. The output result of the white box model 24 into which the input data of the above is input is shown on the proposal screen W1.

For example, on the decision tree model display unit W1a, the white box model 24 created by the white box model creation unit 23 is displayed as the decision tree model 24a at the time of learning when the black box model 15 is created. For the decision tree model 24a shown in the decision tree model display unit W1a, for example, for each branch of the decision tree model 24a using 1000 input data acquired from the input database 13 during the learning period from August to December 2019. The number of cases and the occurrence rate for each event calculated in are displayed.

Further, in the decision tree model display unit W1b, the accuracy of the predicted value of the black box model 15 deteriorates, so that the white box model 24 at the time when the accuracy deterioration factor identification unit 25 identifies the accuracy deterioration factor through the white box model 24. Is displayed as the decision tree model 24a. The decision tree model 24a shown in the decision tree model display unit W1b uses, for example, 500 input data acquired from the input database 13 during the evaluation period from January to March 2020 for each branch of the decision tree model 24a. The number of cases and the occurrence rate for each event calculated in are displayed. Then, on the decision tree model display unit W1b, it is displayed together with the comment 71 that the occurrence rate of the leaf 34 has decreased by 20% at the branch 36 of the decision tree model 24a. As for the leaf 35, the incidence rate has increased from 10% at the time of learning to 20% at the time of measurement, and there is some abnormality.

When the prediction accuracy of the occurrence rate changes in the event shown by the decision tree model 24a in this way, it is presumed that some state change occurs in the branch of the route leading to this event (for example, branch 36).

In the graph display unit W1c, a graph showing the transition of the occurrence rate in the branch 36 in which the occurrence rate has decreased is displayed. The horizontal axis of the graph in the figure represents the date, and the vertical axis represents the incidence. The learning period in which the black box model creation device 14 shown in FIG. 1 reads the input data from the input database 13 in order to create the black box model 15 is from August to December 2019 on the left side of the graph display unit W1c. Between. On the other hand, the determination period in which the control system 1 is put into production and the accuracy of the predicted value of the black box model 15 is determined is between January and March 2020.

Here, it is displayed that the average occurrence rate of the leaf 34 at the time of learning is 50%, whereas the average occurrence rate of the leaf 34 at the time of measurement is 30%. In particular, the incidence of leaf 34 has decreased since February 2020 at the time of measurement, and the predicted value of the black box model 15 has changed.

Therefore, at the lower part of the graph display unit W1c, a comment 72 proposing to relearn with the data after the part where the white box model 24 does not fit (for example, February 2020) is displayed. In this way, the remodeling method proposing unit 26 indicates the factors that change the accuracy of the predicted value specified by the accuracy deterioration factor specifying unit 25, and proposes a change in the conditions of the input data used in the remodeling. In this case, re-modeling method proposed unit 26, shown as the time accuracy is lowered in the predicted value of the black box model 15 when the incidence of the branch 36 is lowered, as the condition of the input data used in remodeling, remodeling It is possible to propose a period for acquiring the input data used in. If the number of re-learning data required for recreating the black box model 15 is small, a proposal may be made to encourage the re-learning data to be accumulated up to the threshold value.

In the analysis system 2 according to the first embodiment described above, the white box model 24 created by imitating the black box model 15 used in the control system 1 is used from the black box model 15 in the normal state. Extract the difference from the output predicted value. Then, the analysis system 2 can provide the service provider with a method for identifying the cause of deterioration in accuracy and remodeling the black box model 15 for improving the accuracy of the predicted value. Therefore, the service provider can urge the customer to recreate the black box model 15 by notifying the customer of the cause of deterioration in accuracy and the remodeling method.

Here, as the white box model 24, for example, the decision tree model 24a in which the input data used in the execution environment of the control system 1 is input and the occurrence rate for each branch is calculated, and the events represented by the input data are clustered. The clustering model 24b or the like is used. In this way, the processing of the black box model 15 is visualized by the white box model 24. Therefore, the service provider can grasp the location where the accuracy of the predicted value of the black box model 15 has changed and how the input data input at the time of measurement is different from that at the time of learning.

Further, as shown in FIG. 10, the service provider can grasp from which point in time the input data should be used for remodeling. Therefore, the service provider can provide the information necessary for the customer to recreate the black box model 15. Then, the customer can recreate the black box model 15 in which the accuracy of the predicted value is improved by using the notified remodeling method, and the recreated black box model 15 can be used in the control system 1.

[Second Embodiment]
Next, the machine learning model accuracy analysis system of the black-boxed machine learning model according to the second embodiment of the present invention will be described. In the machine learning model accuracy analysis system according to the present embodiment, when the occurrence rate of a specific event changes significantly between the acquisition of learning data and the acquisition of input data at the time of measurement, the change in the occurrence rate changes. It proposes re-learning (remodeling method) of the black box model by deleting the large branching condition. As an example of the machine learning model accuracy analysis system according to the second embodiment, the analysis system 2 according to the first embodiment described above is used. Here, a process for proposing a remodeling method for the black box model 15 and a screen for proposing a remodeling method will be described.

FIG. 11 is a flowchart showing an example of a process in which the remodeling method proposing unit 26 according to the second embodiment proposes a remodeling method for the black box model 15. The process shown in FIG. 11 is performed after the accuracy deterioration factor identification process (S5) already described with reference to FIG.

First, the remodeling method proposal unit 26 pays attention to the branch in which the deterioration factor is identified with respect to the new input data used by the accuracy deterioration factor identification unit 25 to identify the accuracy deterioration factor, and every period of this branch. Calculate the accuracy of. This input data is read from the input database 13 by the accuracy deterioration factor identification unit 25 during the determination period. Then, the remodeling method proposal unit 26 compares the accuracy of each branch of the decision tree model between the time of learning and the time of inputting the input data this time, and the item having a large average difference in accuracy (branch condition in this embodiment). Is extracted (S41).

Next, the remodeling method proposal unit 26 proposes to remodel the black box model 15 using the data excluding the extracted items as new learning data (S42). As this proposal, for example, a process of displaying a proposal screen of the remodeling method shown in FIG. 12, which will be described later, is performed. After that, the process returns to FIG. 6, and when the customer approves the proposal of the remodeling method, the process returns to step S1 of FIG. 6 and the black box model 15 is recreated.

<Proposal screen for remodeling method>
FIG. 12 is a diagram showing a display example of the proposal screen W2 of the remodeling method. The proposal screen W2 is displayed, for example, on the display device 65 shown in FIG.

The proposal screen W2 includes decision tree model display units W1a and W1b in the same manner as the proposal screen W1 shown in FIG. The remodeling method proposal unit 26 determines that the output result of the white box model 24 into which the input data used during the training of the black box model 15 is input and the time when the accuracy of the predicted value of the black box model 15 changes significantly are included. The output result of the white box model 24 into which the input data of the period is input is shown on the proposal screen W2.

For example, on the decision tree model display unit W1a, the white box model 24 created by the white box model creation unit 23 is displayed as the decision tree model 24a at the time of learning when the black box model 15 is created. The occurrence rate of the event represented by the node 81 of the decision tree model 24a created at the time of learning is 50%, and the occurrence rate of the event represented by the leaf 82 having a rotation speed of less than 20 times is 65%. It is assumed that the occurrence rate of the event represented by the leaf 83 having 20 or more rotations is 35%. Similarly, the occurrence rate of the event represented by the node 84 of the decision tree model 24a is 20%, the occurrence rate of the event represented by the leaf 85 in which the rotation speed is less than 10 times is 40%, and the rotation speed is 40%. It is assumed that the occurrence rate of the event represented by the leaf 86 in which is 10 times or more is 7%.

In the decision tree model display unit W1b, the white box model 24 at the time when the accuracy deterioration factor identification unit 25 identifies the accuracy deterioration factor through the white box model 24 due to the deterioration of the accuracy of the predicted value of the black box model 15 is displayed. It is displayed as a decision tree model 24a. When the prediction accuracy of the occurrence rate changes in the event shown by the decision tree model 24a, it is presumed that some state change occurs in the branch of the route leading to this event (for example, branches 87 and 88). In branch 87, comment 91 shows that the rate of occurrence of the item "rotation speed" decreased by 35% on the left side of branch 87 and increased by 15% on the right side of branch 87. Then, in branch 87, the average change in the occurrence rate of the item "rotation speed" is calculated as 25% (= (35% + 15%) / 2).

Similarly, in branch 88, comment 92 shows that the rate of occurrence of the item "rotation speed" decreased by 25% on the left side of branch 88 and increased by 23% on the right side of branch 88. Then, in the branch 88, the average change in the occurrence rate of the item "rotation speed" is calculated as 24% (= (25% + 23%) / 2). Therefore, the overall average of the change in the occurrence rate of the item "rotation speed" of the branches 87 and 88 is calculated as 24.5% (= (25% + 24%) / 2). As described above, if the overall average of the change in the occurrence rate of the item "rotation speed" is equal to or more than the threshold value (for example, 15%), the item "rotation speed" is not suitable for the configuration of the decision tree model 24a. Therefore, at the bottom of the proposal screen W2, we recommend re-learning by excluding the item "rotation speed" in which the average change in the incidence rate is 24.5%. The comment 93 is displayed. Thus re-modeling method proposed unit 26, the factors identified by the accuracy deterioration factor specifying unit 25 proposes to delete the branch condition change in incidence is greater than the threshold value. Then, the service provider can recommend the customer to relearn the black box model 15 by excluding the item "rotation speed".

Here, the exclusion of the item "rotation speed" proposed by the remodeling method proposal unit 26 is to exclude any of the branch 87 divided by 20 times and the branch 88 divided by 10 times to form a decision tree model. It is to reconstruct. However, the composition of the decision tree that has already been constructed is not particularly conscious, and the process of recreating the decision tree without simply adding the item "rotation speed" to the explanatory variable is the process of recreating the decision tree in the black box model creation device 14 and the white box model creation unit 23. Will be done by.

In the process of recreating the black box model 15, the change point of the branching accuracy in which the deterioration factor is specified by the accuracy deterioration factor identification unit 25 extracted from the input database 13 by the proposal learning data extraction unit 27 in step S1 of FIG. Subsequent input data is used as re-learning data. Then, the black box model creating device 14 deletes the branching condition in which the change in the event occurrence rate is large, that is, excludes the item "rotation speed", and recreates the black box model 15. Further, the white box model creating unit 23 also recreates the white box model 24 by deleting the branching condition in which the change in the event occurrence rate is large. At the time of subsequent measurement, the recreated black box model 15 is used in the control system 1.

In the analysis system 2 according to the second embodiment described above, the accuracy of the predicted value of the black box model 15 deteriorated due to a large change in the occurrence rate of branch items at the time of measurement as compared with the time of learning. If found, it is proposed to remove this branching condition and relearn (remodel) the black box model 15. Therefore, the customer recreates the black box model 15 with improved accuracy of the predicted value by using the remodeling method notified by the service provider, and uses the recreated black box model 15 in the control system 1. be able to. Further, the service provider can also delete the branching condition caused by the deterioration of the accuracy of the predicted value of the black box model 15 and recreate the white box model 24 to visualize the processing of the control system 1. it can.

Further, the remodeling method proposal unit 26 may attach the graph display unit W1c shown in the decision tree model display unit W1a of FIG. 10 to the decision tree model display unit W1b of FIG. 12 for display. By displaying the graph display unit W1c, the service provider can easily grasp from what point in time the input data should be used for remodeling.

[Modification example]
In the above-described embodiment, the analysis system 2 includes the accuracy determination unit 21 and the proposed learning data extraction unit 27, but the control system 1 has at least one of the accuracy determination unit 21 and the proposal learning data extraction unit 27. The configuration may include one. In such a configuration, the analysis system 2 processes the accuracy deterioration factor identification unit 25 and the remodeling method proposal unit 26 at the timing when the accuracy determination unit 21 operating in the control system 1 outputs the defect accuracy determination result. May be done.

Further, the analysis system 2 may have the black box model 15 in the analysis system 2 as a configuration including the black box model creation device 14. Then, the service provider may provide a service that provides the customer with the black box model 15.

Further, in the above-described embodiment, the input data used for creating the black box model 15 and the white box model 24 is read out during the learning period, and then the accuracy of the predicted value output from the black box model 15 changes. It was decided that the input data read during the judgment period would be used. However, the learning period and the determination period may overlap.

It should be noted that the present invention is not limited to the above-described embodiment, and it goes without saying that various other application examples and modifications can be taken as long as the gist of the present invention described in the claims is not deviated.
For example, the above-described embodiment describes the configuration of the system in detail and concretely in order to explain the present invention in an easy-to-understand manner, and is not necessarily limited to the one including all the described configurations. Further, it is also possible to add, delete, or replace a part of the configuration of the present embodiment with another configuration.
In addition, the control lines and information lines indicate those that are considered necessary for explanation, and do not necessarily indicate all the control lines and information lines in the product. In practice, it can be considered that almost all configurations are interconnected.

1 ... Control system, 2 ... Analysis system, 13 ... Input database, 14 ... Black box model creation device, 15 ... Black box model, 16 ... Accuracy data storage database, 21 ... Accuracy judgment unit, 22 ... Verification unit, 23 ... White Box model creation department, 24 ... White box model, 25 ... Accuracy deterioration factor identification department, 26 ... Remodeling method proposal department, 27 ... Proposal learning data extraction department

Claims (13)

It is learned based on the input data read in a preset learning period from the input database in which the input data including the result of the processing performed by the control device for the controlled device used in the control system is accumulated. The black-boxed machine learning model is created in accordance with the creation of the black-boxed machine learning model that can output the result of the processing as a predicted value from the input data. A white box model creation unit that creates a white box model using the input data used to create the model as an explanatory variable and the predicted value output by the black boxed machine learning model as an objective variable.
When it is determined that the accuracy of the predicted value output from the black-boxed machine learning model has changed during the determination period in which the accuracy of the predicted value of the black-boxed machine learning model is determined, A machine learning model accuracy analysis system including a factor identification unit that inputs the input data accumulated in the determination period into the white box model and identifies a factor that changes the accuracy of the predicted value. A precision determination unit for determining that the accuracy of the predicted value has deteriorated when the accuracy of the predicted value output from the black-boxed machine learning model changes from the threshold value during the determination period is provided.
The machine learning model accuracy analysis system according to claim 1, wherein the factor identification unit identifies a factor that changes the accuracy of the predicted value when the accuracy determination unit determines that the accuracy of the predicted value has deteriorated. .. The machine learning model accuracy according to claim 2, further comprising a proposal unit that proposes a method for remodeling the black-boxed machine learning model when a factor whose accuracy of the predicted value has changed is identified by the factor identification unit. Analysis system. The proposal unit indicates a factor whose accuracy of the predicted value specified by the factor identification unit has changed, is input to the black-boxed machine learning model, and changes the conditions of the input data used in remodeling. The machine learning model accuracy analysis system according to claim 3. The proposal unit describes the output result of the white box model to which the input data used at the time of learning the black boxed machine learning model is input and the predicted value of the black boxed machine learning model. The machine learning model accuracy analysis system according to claim 4, which indicates the output result of the white box model in which the input data of the determination period including the time when the accuracy changes is created. The input data extracted from the input database based on the remodeling method of the black-boxed machine learning model instructed by the proposal unit is black-boxed to create the black-boxed machine learning model. The machine learning model accuracy analysis system according to claim 3, further comprising an extraction unit that outputs to the machine learning model creation device. The white box model is represented by a decision tree model that identifies the event by a decision tree that branches an event that appears in the controlled device at a predetermined occurrence rate under a predetermined condition.
The factor identification unit inputs the input data accumulated during the determination period into the decision tree model, calculates the occurrence rate of the event for each branch of the decision tree, and calculates the occurrence rate of the event.
When the amount of change between the event occurrence rate calculated during the learning period and the event occurrence rate calculated during the determination period becomes larger than the threshold value, the branching condition of the decision tree is set. The machine learning model accuracy analysis system according to any one of claims 1 to 6, which is specified as a factor that changes the accuracy of the predicted value. The proposed unit indicates a time when the accuracy of the predicted value of the black boxed machine learning model is changed, as a condition of the input data used in remodeling, the input data used in the previous SL remodeling The machine learning model accuracy analysis system according to claim 7, which proposes an acquisition period. The proposed unit, by the factor specified by the previous SL factor specifying unit, machine learning model accuracy analysis system according to claim 7 in which the change in the incidence propose the deletion of branch condition is greater than the threshold value. The white box model is represented by a clustering model in which events appearing in the controlled device are classified into clusters divided into subsets having the same characteristics.
The factor identification unit inputs the input data accumulated during the determination period into the clustering model, calculates the occurrence rate of the event for each cluster, and calculates the occurrence rate of the event.
When the amount of change between the event occurrence rate calculated during the learning period and the event occurrence rate calculated during the determination period becomes larger than the threshold value, the change amount becomes large. For the cluster, the distance between the centers with the closest cluster is calculated.
The machine learning model accuracy analysis system according to any one of claims 1 to 6, wherein the explanatory variable having the largest distance component at the distance is specified as a factor that changes the accuracy of the predicted value. The machine learning model accuracy analysis system according to claim 1, wherein the process performed by the control device on the controlled device includes a process of measuring a workpiece processed by the controlled device. A black box is created in which learning is performed based on the input data read in a preset learning period from the input database that stores the results of the processing performed by the control device for the controlled device used in the control system. It is a machine learning model and is used for creating the black boxed machine learning model in accordance with the creation of the black boxed machine learning model that can output the result of the processing as a predicted value from the input data. A step of creating a white box model using the input data as an explanatory variable and a predicted value output by the black boxed machine learning model as an objective variable.
When it is determined that the accuracy of the predicted value output from the black-boxed machine learning model has changed during the determination period in which the accuracy of the predicted value of the black-boxed machine learning model is determined, A machine learning model accuracy analysis method including a step of inputting the input data accumulated in the determination period into the white box model and identifying a factor that changes the accuracy of the predicted value. A black box is created in which learning is performed based on the input data read in a preset learning period from the input database that stores the results of the processing performed by the control device for the controlled device used in the control system. It is a machine learning model and is used for creating the black boxed machine learning model in accordance with the creation of the black boxed machine learning model that can output the result of the processing as a predicted value from the input data. A procedure for creating a white box model using the input data as an explanatory variable and a predicted value output by the black boxed machine learning model as an objective variable.
When it is determined that the accuracy of the predicted value output from the black-boxed machine learning model has changed during the determination period in which the accuracy of the predicted value of the black-boxed machine learning model is determined, A program for inputting the input data accumulated in the determination period into the white box model and causing a computer to execute a procedure for identifying a factor that changes the accuracy of the predicted value.

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