WO2019148669A1 - Method and apparatus for generating machine learning model, computer device, and storage medium - Google Patents

Abstract

Embodiments of the present application disclose a method and apparatus for generating a machine learning model, a computer device, and a storage medium. The method comprises: obtaining training data; invoking a pre-stored data processing module and a pre-stored algorithm module; performing data preprocessing on the training data according to the pre-stored data processing module; training and validating the pre-stored algorithm module according to the processed training data to obtain a machine learning model and a corresponding validation index; and displaying the machine learning model and the corresponding validation index.

Description

Method, device, computer equipment and storage medium for generating machine learning model

This application claims the priority of the Chinese Patent Application filed on January 30, 2018, the Chinese Patent Office, Application No. 201810089701.5, and the invented name of the "Machine Learning Model Generation Method, Apparatus, Computer Equipment, and Storage Medium". This is incorporated herein by reference.

Technical field

The present application relates to the field of machine learning technologies, and in particular, to a method, an apparatus, a computer device, and a storage medium for generating a machine learning model.

Background technique

The machine learning process generally includes a data preprocessing process and a model training process. Both the data pre-processing process and the model training process require engineers to implement the code. However, in many cases, the data preprocessing methods used in the machine learning process, the algorithms for model training, etc. all have high similarities. If you need engineers to write code every time, it will inevitably bring a lot of work to the engineers, causing repetitive work and wasting time.

Summary of the invention

The embodiment of the present application provides a method, a device, a computer device, and a storage medium for generating a machine learning model, which can quickly obtain a machine learning model.

In a first aspect, the embodiment of the present application provides a method for generating a machine learning model, including: acquiring training data; acquiring a pre-stored data processing module and a pre-stored algorithm module; and performing the training data according to the pre-stored data processing module. Data pre-processing to obtain processed training data; training and verifying the pre-stored algorithm module according to the processed training data to obtain a machine learning model and a verification indicator corresponding to the machine learning model; and displaying according to a preset The rules display the machine learning model and its corresponding verification metrics.

In a second aspect, an embodiment of the present application provides a device for generating a machine learning model, including:

a data acquisition unit, configured to acquire training data;

a module retrieval unit, configured to retrieve a pre-stored data processing module and a pre-stored algorithm module;

a data processing unit, configured to perform data preprocessing on the training data according to the pre-stored data processing module to obtain processed training data;

a model generating unit, configured to perform training and verification on the pre-stored algorithm module according to the processed training data to obtain a machine learning model and a verification indicator corresponding to the machine learning model;

And a display unit, configured to display the machine learning model and its corresponding verification indicator according to a preset display rule.

In a third aspect, the embodiment of the present application further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program The method for generating a machine learning model according to any one of the embodiments of the present application is implemented.

In a fourth aspect, the embodiment of the present application further provides a storage medium, wherein the storage medium stores a computer program, where the computer program includes program instructions, and the program instructions, when executed by the processor, cause the processor to execute The method for generating a machine learning model according to any one of the embodiments of the present application.

The method for generating a machine learning model provided by the embodiment of the present application does not need to acquire a machine learning model through programming every time, thereby greatly reducing the workload of the engineer, saving the time of the engineer, and improving the efficiency of acquiring the machine learning model.

DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly described below. Obviously, the drawings in the following description are some embodiments of the present application, For the ordinary technicians, other drawings can be obtained based on these drawings without any creative work.

FIG. 1 is a schematic flowchart of a method for generating a machine learning model according to an embodiment of the present application;

2 is a schematic diagram of a specific process of a method for generating a machine learning model shown in FIG. 1;

FIG. 3 is another schematic flowchart of a method for generating a machine learning model according to an embodiment of the present disclosure;

4 is a schematic structural diagram of a preset user operation interface according to an embodiment of the present application;

FIG. 5 is a schematic block diagram of a device for generating a machine learning model according to an embodiment of the present disclosure;

FIG. 6 is a schematic block diagram of a device for generating a machine learning model according to an embodiment of the present disclosure;

FIG. 7 is a schematic block diagram of a device for generating a machine learning model according to an embodiment of the present disclosure;

FIG. 8 is a schematic block diagram of a computer device according to an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application are clearly and completely described in the following with reference to the drawings in the embodiments of the present application. It is obvious that the described embodiments are a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without departing from the inventive scope are the scope of the present application.

Please refer to FIG. 1. FIG. 1 is a schematic flowchart of a method for generating a machine learning model according to an embodiment of the present application. The method is applied to terminals such as desktop computers, laptop computers, and tablet computers. As shown in FIG. 1, the method includes steps S101 to S105.

S101. Obtain training data.

In this embodiment, the terminal can interact with the user by using a preset user operation interface to obtain training data. For example, the user can input the storage path of the training data into the corresponding input box by pressing a button such as “Browse” in the user operation interface, so that after the user clicks the “confirm submit” button, the terminal acquires the corresponding according to the storage path. Training data.

In an embodiment, the training data can be historical registration data. For example, the training data can be used to register the user name, mobile phone number, etc. of the Lujin Institute financial platform. The training data can also be transaction data. For example, the training data is transaction data for a user to purchase a wealth management product or the like. Of course, the training data can also be other data, and no specific restrictions are imposed here.

S102: Acquire a pre-stored data processing module and a pre-stored algorithm module.

In this embodiment, the pre-stored data processing module and the pre-stored algorithm module are pre-stored in the terminal. The pre-stored data processing module may be a module encapsulated by a data processing method commonly used in a machine learning process. For example, the pre-stored data processing module may be a module encapsulated by methods such as an outlier detection method and a continuous value discretization method. Similarly, the pre-stored algorithm module can be a module encapsulated by algorithms commonly used in the model training process. For example, the pre-stored algorithm module may be a module encapsulated by a classification algorithm, a regression algorithm, or the like. Specifically, the pre-stored algorithm module may be a module encapsulated by a logistic regression algorithm, an SVM algorithm, a decision tree algorithm, or the like.

In an embodiment, the pre-stored data processing module and the pre-stored algorithm module may be a shell script. That is to say, some commonly used data processing methods, algorithms, etc. are pre-packaged into a shell script, and then stored in the terminal for subsequent calls.

In an embodiment, the terminal needs to acquire the data processing module identification information and the algorithm module identification information before the pre-stored data processing module and the pre-stored algorithm module are retrieved. Specifically, the pre-stored data processing module identification information in the terminal is obtained as the data processing module identification information, and the pre-stored algorithm module identification information in the terminal is obtained as the algorithm module identification information. That is to say, at this time, the user does not need to input the data processing module identification information and the algorithm module identification information.

Correspondingly, when the terminal retrieves the pre-stored data processing module and the pre-stored algorithm module, the method includes: acquiring a corresponding pre-stored data processing module according to the data processing module identification information; and acquiring corresponding pre-stored according to the algorithm module identification information Algorithm module.

In an embodiment, the terminal stores pre-stored data processing module and pre-stored algorithm module corresponding identification information, which are respectively referred to as pre-stored data processing module identification information and pre-stored algorithm module identification information. The pre-stored data processing module identification information and the pre-stored algorithm module identification information may both be names of methods or algorithms. Of course, in other embodiments, the pre-stored data processing module identification information and the pre-stored algorithm module identification information may also be other information that plays a role of identification, and is not specifically limited herein.

Generally, the number of the pre-stored data processing module and the pre-stored algorithm module stored in the terminal is at least one, that is, the number of the acquired data processing module identification information and the algorithm module identification information is at least one. Therefore, before the terminal retrieves the pre-stored data processing module and the pre-stored algorithm module, the terminal further needs to arrange all the data processing module identification information and all the algorithm module identification information to form at least one group of machine learning groups; and sequentially read Data processing module identification information and algorithm module identification information in each group of the machine learning group.

For example, the pre-stored data processing module stored in the terminal includes an outlier detection method module and a continuous value discretization method module, and the pre-stored algorithm module includes a logistic regression algorithm module and an SVM algorithm module. That is, the number of pre-stored data processing modules and pre-stored algorithm modules is two. Then, after obtaining the training data, all the pre-stored data processing module identification information in the reading terminal is used as all the data processing module identification information and all the pre-stored algorithm module identification information as all the algorithm module identification information, and are arranged and combined to form four groups of machines. Study group. The four groups of machine learning groups are shown in Table 1.

Table 1

Machine learning group	Data processing module identification information	Algorithm module identification information
First group of machine learning groups	Outlier detection method	Logistic regression algorithm

Second group of machine learning groups	Outlier detection method	SVM algorithm
The third group of machine learning groups	Continuous value discretization method	Logistic regression algorithm
The fourth group of machine learning groups	Continuous value discretization method	SVM algorithm

After forming the four sets of machine learning groups as shown in Table 1, the data processing module identification information and the algorithm module identification information in each group of machine learning groups are sequentially read, and the corresponding modules are retrieved, and then step S103 and steps are performed. S104. For example, the data processing module identification information and the algorithm module identification information in the first group of machine learning groups are first read, that is, the outlier detection method and the logistic regression algorithm respectively; then, the corresponding outlier detection is retrieved according to the outlier detection method. The method module and the corresponding logistic regression algorithm module are retrieved according to the logistic regression algorithm; and then the steps S103 to S104 are performed according to the corresponding outlier detection method module and the logistic regression algorithm module in the first group of machine learning groups to obtain the first group of machines The machine learning model corresponding to the learning group and the corresponding verification indicators. Then, the process returns to perform reading of the data processing module identification information and the algorithm module identification information in the second group of machine learning groups, that is, the abnormal value detection method and the SVM algorithm, and the like, until four machine learning models and corresponding verifications are obtained. Until the indicator.

Of course, in other embodiments, when at least two pre-stored data processing modules and/or at least two pre-stored algorithm modules are stored in the terminal, when the pre-stored data processing module and the pre-stored algorithm module are retrieved, one may be randomly selected. The data processing module and a pre-stored algorithm module are pre-stored and steps S103 to S104 are performed. That is to say, it is not necessary to arrange and combine all the pre-stored data processing modules and all the pre-stored algorithm modules stored in the terminal. In this case, only one machine learning model and corresponding verification indicators are obtained, and no specific limitation is imposed herein.

S103. Perform data preprocessing on the training data according to the pre-stored data processing module to obtain processed training data.

For example, if the pre-stored data processing module acquired in step S102 is an abnormal value detecting method module, the terminal performs pre-processing of the abnormal value detecting data on the training data by using the abnormal value detecting method module to obtain the processed training data.

S104. Train and verify the pre-stored algorithm module according to the processed training data to obtain a machine learning model and a verification indicator corresponding to the machine learning model.

After performing data pre-processing on the training data, the pre-stored algorithm module obtained in step S102 is trained and verified by using the processed training data, thereby obtaining a machine learning model and corresponding verification indicators.

Specifically, in an embodiment, as shown in FIG. 2, FIG. 2 is a specific schematic flowchart of a method for generating a machine learning model shown in FIG. 1. This step S104 includes steps S1041 to S1043.

S1041: The processed training data is divided into training model data and verification model data according to a preset ratio.

For example, the processed training data is divided into training model data and verification model data according to a preset ratio of 9 to 1. That is to say, 90% of the processed training data is used as the training model data, and 10% of the processed training data is used as the verification model data.

S1042. Train the pre-stored algorithm module according to the training model data to obtain a machine learning model.

In an embodiment, when the pre-stored algorithm is encapsulated into a module such as a shell script, the algorithm parameters corresponding to the pre-stored algorithm are also encapsulated in the pre-stored algorithm module. When the training model data is obtained, the algorithm parameters in the pre-stored algorithm module are brought to the corresponding positions of the corresponding pre-stored algorithms to form an initial model, and then the initial model is trained with the training model data to obtain a final machine learning model. That is to say, the pre-stored algorithm module includes default algorithm parameters. When the user does not set the algorithm parameters, the terminal forms the initial model by using the default algorithm parameters in the pre-stored algorithm module. Wherein, for the logistic regression algorithm, the algorithm parameters may include a step size and an iteration number. For the decision tree algorithm, the algorithm parameters may include tree depth, maximum split feature number, and non-existence. The algorithm parameters may be set according to a specific algorithm in the corresponding pre-stored algorithm module, and no specific limitation is imposed herein.

S1043. Verify the machine learning model according to the verification model data to obtain a verification indicator corresponding to the machine learning model.

After obtaining the machine learning model, the obtained machine learning model will be verified with the verification model data to obtain corresponding verification indicators. The verification indicator may be a recall rate, an accuracy rate, etc., and no specific restrictions are imposed here.

S105. Display the machine learning model and its corresponding verification indicator according to a preset display rule.

Specifically, in an embodiment, the identification information of the machine learning model and the verification indicator corresponding to the machine learning model are sequentially displayed according to the size of the verification indicator. For example, when two pre-stored data processing modules and two pre-stored algorithm modules are acquired in step S102, after steps S103 to S104, four machine learning models and corresponding verification indicators are obtained. Assuming that the verification index is the accuracy rate, the terminal can obtain the order of the four machine learning models in descending order of accuracy, as shown in Table 2.

Table 2

In Table 2, the identification information of the machine learning model may be the number of each machine learning model, which is "001", "002", "003", and "004", respectively. Of course, in other embodiments, the identification information of the machine learning model may also be other identifiers for distinguishing from each other, and no specific limitation is imposed herein.

The terminal can display the information about the four machine learning models in the form of Table 2, so that the user can see the accuracy rate corresponding to each machine learning model through Table 2, so that the user can select the appropriate machine learning model according to the needs. Subsequent forecasts.

In an embodiment, the terminal may further display data processing module identification information and algorithm module identification information corresponding to each machine learning model, as shown in Table 2, so that the user can better understand the generation process of the machine learning model. Information such as the data processing method used and the algorithm used in model training. Of course, the terminal may also display the algorithm parameters corresponding to the pre-stored algorithm module in each machine learning model finally obtained, and the display content is not limited herein.

It should be noted that, in other embodiments, the preset display rules are not limited to the above-mentioned order of display according to the size of the verification indicator, and may also be other types of display rules. For example, the preset display rule may also be the identification information of the machine learning model that displays the best verification indicator and the corresponding verification indicator. As shown in Table 2, the highest accuracy of the four machine learning models is 95%. Therefore, the terminal can display the machine learning model with the identification information of the machine learning model as 001 and the corresponding accuracy rate.

In this embodiment, the method for generating the machine learning model does not need to acquire the machine learning model through programming every time, which can greatly reduce the workload of the engineer and improve the efficiency of acquiring the machine learning model. At the same time, for non-computer users such as salesmen, it is only necessary to submit the training data to the terminal to quickly acquire at least one machine learning model, so that the machine learning model can be obtained in a simpler manner and is applicable to a wider population.

Please refer to FIG. 3. FIG. 3 is a schematic flowchart of a method for generating a machine learning model according to an embodiment of the present application. The method is applied to terminals such as desktop computers, laptop computers, and tablet computers. As shown in Fig. 3, the method includes steps S201 to S208.

S201. Obtain training data, data processing module identification information, and algorithm module identification information.

In this embodiment, the terminal may interact with the user by using a preset user operation interface, thereby acquiring training data, data processing module identification information, and algorithm module identification information input by the user. For example, as shown in FIG. 4, FIG. 4 is a schematic structural diagram of a preset user operation interface according to an embodiment of the present application. The preset user operation interface 10 is provided with a "data processing module identification information selection column", an "algorithm module identification information selection column", and the like. The user can select the data processing method and algorithm to be used in the "data processing module identification information selection column" and "algorithm module identification information selection column". The user can click the “Browse” function button, so that the user can input the storage path of the training data into the corresponding input box, and then click the “confirm submit” button, so that the terminal can obtain the user by preset the user operation interface 10. The input data processing module identification information, the algorithm module identification information, and the storage path of the training data, and the corresponding training data are obtained according to the storage path of the training data.

In an embodiment, the training data may be historical registration data, transaction data, etc., and is not specifically limited herein. In addition, the data processing module identification information may be a name corresponding to the data processing method. For example, the data processing module identification information may be an abnormal value detection method. Similarly, the algorithm module identification information may be a name corresponding to the algorithm used in the model training. For example, the algorithm module identification information may be a logistic regression algorithm. Of course, in other embodiments, the data processing module identification information and the algorithm module identification information may also adopt other identification information, which is not specifically limited herein.

It should be noted that the manner in which the terminal acquires the training data, the data processing module identification information, and the algorithm module identification information input by the user is not limited to the manner shown in FIG. 4, and may be other modes. The manner shown in FIG. 4 is only There is one of many ways to obtain, and no specific restrictions are imposed here. In this embodiment, for some professional users in the field of computers and the like, the user can input the required data processing module identification information and algorithm module identification information in a manner similar to that shown in FIG. In this way, the user can set the data processing method and the algorithm used for model training according to the individual's experience and project requirements, etc., to meet the acquisition requirements of the machine learning model of the professional user population.

In an embodiment, the user may also set algorithm parameters corresponding to the algorithm module identification information. Specifically, the acquiring the training data, the data processing module identification information, and the algorithm module identification information, further includes: acquiring the algorithm parameter corresponding to the identifier information of the algorithm module. In this way, the user can set the algorithm parameters in the algorithm module corresponding to the algorithm module identification information according to personal experience, and the machine learning model can be obtained faster and better. For the logistic regression algorithm, the algorithm parameters may include the step size and the number of iterations. For the decision tree algorithm, the algorithm parameters may include tree depth, maximum split feature number, and non-existence. The algorithm parameters may be set according to a specific algorithm in the corresponding pre-stored algorithm module, and no specific limitation is imposed herein. In this embodiment, after the terminal passes the step S201, the number of the data processing module identification information and the algorithm module identification information acquired are at least one.

S202. Arrange and combine all the data processing module identification information and all the algorithm module identification information to form at least one group of machine learning groups.

For example, if the number of data processing module identification information and algorithm module identification information is two, then four groups of machine learning groups can be formed after the combination. Each group of machine learning groups includes a data processing module identification information and an algorithm module identification information.

S203. Read data processing module identification information and algorithm module identification information in each group of the machine learning group in sequence.

In this embodiment, after reading the data processing module module identification information and the algorithm module identification information in a group of machine learning groups, step S204 to step S207 are performed. Then, the data processing module module identification information and the algorithm module identification information in the next group of machine learning groups are read, and then steps S204 to S207 are performed until the machine learning model corresponding to each group of machine learning groups and the corresponding verification indicators are obtained. .

S204. Acquire a corresponding pre-stored data processing module according to the data processing module identification information.

Specifically, the terminal matches the data processing module identification information input by the user with the pre-stored data processing module identification information in the terminal, and retrieves the pre-stored data processing module corresponding to the matched identification information. The identifier information of the pre-stored data processing module may be a name corresponding to the data processing method. For example, the identifier information of the pre-stored data processing module may be an abnormal value detection method.

S205. Acquire a corresponding pre-stored algorithm module according to the algorithm module identification information.

Specifically, the terminal matches the algorithm module identification information input by the user with the pre-stored algorithm module identification information in the terminal, and retrieves the pre-stored algorithm module corresponding to the matched identification information. The pre-stored algorithm module identification information may be a name corresponding to the algorithm. For example, the pre-stored algorithm module identification information may be a logistic regression algorithm.

S206. Perform data preprocessing on the training data according to the pre-stored data processing module to obtain processed training data.

For example, if the pre-stored data processing module acquired in step S204 is an abnormal value detecting method module, the terminal performs data pre-processing on the training data by using the abnormal value detecting method module to obtain the processed training data.

S207. Train and verify the pre-stored algorithm module according to the processed training data to obtain a machine learning model and a verification indicator corresponding to the machine learning model.

After preprocessing the data of the training data, the pre-stored algorithm module acquired in S205 is trained and verified by using the processed training data, thereby obtaining a machine learning model and corresponding verification indicators.

In an embodiment, when the algorithm parameter corresponding to the algorithm module identification information is acquired in step S201, step S207 specifically includes: training and verifying the pre-stored algorithm module by setting the algorithm parameter according to the processed training data. To obtain a machine learning model and a verification indicator corresponding to the machine learning model.

Specifically, the algorithm parameters set by the user are brought into the corresponding positions of the pre-stored algorithm module to form an initial model, and then the initial model is trained with the processed training data to obtain a final machine learning model. That is to say, when the user sets the algorithm parameters, the algorithm parameters set by the user are brought into the pre-stored algorithm module to form an initial model.

After obtaining the machine learning model, the machine learning model needs to be verified with the processed training data to obtain the verification index. Among them, the verification indicators include recall rate, accuracy rate, etc., and no specific restrictions are imposed here.

S208. Display the machine learning model and its corresponding verification indicator according to a preset display rule.

Specifically, in an embodiment, the identification information of the machine learning model and the verification indicator corresponding to the machine learning model are sequentially displayed according to the size of the verification indicator.

The identification information of the machine learning model may be a number of each machine learning model, for example, the number “001”, the number “002”, and the like. The identification information of the machine learning model may also be the name of the machine learning model, for example, "Logistic Regression Machine Learning Model 1", "Logistic Regression Machine Learning Model 2", etc., and no specific restrictions are imposed herein.

It should be noted that the preset display rules are not limited to the above-mentioned order according to the size of the verification index, and may also be other kinds of display rules. For example, the preset display rule may also be the identification information of the machine learning model that displays the best verification indicator and the corresponding verification indicator. For example, when the verification index is the accuracy rate, the identification information of a machine learning model with the highest accuracy rate in multiple machine learning models and the corresponding accuracy rate can be displayed.

In an embodiment, the terminal may further display data processing module identification information and algorithm module identification information corresponding to each machine learning model, so that the user can better understand the data processing method used in the generation process of the machine learning model. And information such as algorithms used in model training. Of course, the terminal can also display the algorithm parameters corresponding to the pre-stored algorithm module in each machine learning model finally obtained, and the display content is not limited herein.

In this embodiment, the method for generating the machine learning model does not need to acquire the machine learning model through programming every time, which can greatly reduce the workload of the engineer and improve the efficiency of acquiring the machine learning model. At the same time, for professional users in the computer field, data processing methods, model training algorithms, algorithm parameters and other information can be set according to personal experience, project requirements, etc., so that the terminal quickly acquires the corresponding machine learning model according to the user's settings. It makes the acquisition of the machine learning model more simple and convenient.

The embodiment of the present application further provides a device for generating a machine learning model, where the device for generating a machine learning model is configured to execute a method for generating a machine learning model. Specifically, please refer to FIG. 5. FIG. 5 is a schematic block diagram of a device for generating a machine learning model according to an embodiment of the present application. The machine learning model generating device 300 can be installed in a desktop computer, a tablet computer, a laptop computer, or the like.

As shown in FIG. 5, the machine learning model generating apparatus 300 includes a data acquiring unit 301, a module calling unit 302, a data processing unit 303, a model generating unit 304, and a display unit 305.

The data acquisition sheet 301 is used to acquire training data.

The module retrieving unit 302 is configured to retrieve a pre-stored data processing module and a pre-stored algorithm module.

In an embodiment, the data acquisition unit 301 is further configured to acquire data processing module identification information and algorithm module identification information. Specifically, the data acquisition unit 301 acquires the pre-stored data processing module identification information in the terminal as the data processing module identification information, and acquires the pre-stored algorithm module identification information in the terminal as the algorithm module identification information. Correspondingly, the module retrieving unit 302 is configured to retrieve a corresponding pre-stored data processing module according to the data processing module identification information; and retrieve a corresponding pre-stored algorithm module according to the algorithm module identification information.

Generally, the number of pre-stored data processing modules and pre-stored algorithm modules stored in the terminal is at least one. As shown in FIG. 6, FIG. 6 is another schematic block diagram of a device for generating a machine learning model according to an embodiment of the present application. The machine learning model generating device 300 further includes an arrangement combining unit 306 and a reading unit 307. The arrangement combining unit 306 is configured to arrange all the data processing module identification information and all the algorithm module identification information into at least one set of machine learning groups. The reading unit 307 is configured to sequentially read data processing module identification information and algorithm module identification information in each group of the machine learning group. The module retrieving unit 302 retrieves the corresponding pre-stored data processing module according to the data processing module identification information read by the reading unit 307, and retrieves the corresponding pre-stored algorithm module according to the algorithm module identification information read by the reading unit 307. .

The data processing unit 303 is configured to perform data preprocessing on the training data according to the pre-stored data processing module to obtain processed training data.

The model generating unit 304 is configured to perform training and verification on the pre-stored algorithm module according to the processed training data to obtain a machine learning model and a verification indicator corresponding to the machine learning model.

Specifically, in an embodiment, as shown in FIG. 6, the model generation unit 304 includes a division subunit 3041, a training subunit 3042, and a verification subunit 3043.

The dividing subunit 3041 is configured to divide the processed training data into training model data and verification model data according to a preset ratio.

The training subunit 3042 is configured to train the pre-stored algorithm module according to the training model data to obtain a machine learning model.

The verification subunit 3043 is configured to verify the machine learning model according to the verification model data to obtain a verification indicator corresponding to the machine learning model.

The display unit 305 is configured to display the machine learning model and its corresponding verification indicator according to a preset display rule.

Specifically, in an embodiment, the display unit 305 sequentially displays the identification information of the machine learning model and the verification indicator corresponding to the machine learning model according to the size of the verification indicator.

In an embodiment, the display unit 305 can also display the data processing module identification information and the algorithm module identification information corresponding to each machine learning model, so that the user can better understand the data processing used in the process of generating the machine learning model. The method and the algorithm used in the model training. Of course, the display unit 305 can also display the algorithm parameters corresponding to the pre-stored algorithm modules in each machine learning model finally obtained, and the display content is not limited herein.

It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of the description, the above-mentioned machine learning model generating apparatus 300 and the specific working process of each unit can refer to the foregoing machine learning model generating method. Corresponding processes in the embodiments are not described herein again. In this embodiment, the machine learning model generating apparatus 300 can improve the efficiency of acquiring the machine learning model, and at the same time, make the machine learning model acquisition mode simpler and applicable to a wider population.

Referring to FIG. 7, FIG. 7 is a schematic block diagram of a device for generating a machine learning model according to an embodiment of the present application. The machine learning model generating device 400 can be installed in a desktop computer, a tablet computer, a laptop computer, or the like.

As shown in FIG. 7, the machine learning model generating apparatus 400 includes a data acquiring unit 401, an array combining unit 402, a reading unit 403, a module calling unit 404, a data processing unit 405, a model generating unit 406, and a display unit 407.

The data obtaining unit 401 is configured to acquire training data, data processing module identification information, and algorithm module identification information.

In an embodiment, the data acquiring unit 401 is further configured to: acquire algorithm parameters corresponding to the algorithm module identification information. In this way, the user can set the algorithm parameters in the algorithm module corresponding to the algorithm module identification information according to personal experience, and the machine learning model can be obtained faster and better.

It can be understood that, in this embodiment, the number of data processing module identification information and algorithm module identification information acquired by the data acquiring unit 401 is at least one.

The arranging unit 402 is configured to arrange all the data processing module identification information and all the algorithm module identification information into at least one set of machine learning groups.

The reading unit 403 is configured to sequentially read data processing module identification information and algorithm module identification information in each group of the machine learning group.

The module retrieving unit 404 is configured to retrieve a corresponding pre-stored data processing module according to the data processing module identification information, and retrieve a corresponding pre-stored algorithm module according to the algorithm module identification information.

The data processing unit 405 is configured to perform data preprocessing on the training data according to the pre-stored data processing module to obtain processed training data.

The model generating unit 406 is configured to perform training and verification on the pre-stored algorithm module according to the processed training data to obtain a machine learning model and a verification indicator corresponding to the machine learning model.

In an embodiment, when the data acquisition unit 401 acquires the algorithm parameter corresponding to the algorithm module identification information, the model generation unit 406 is specifically configured to set the algorithm parameter to the pre-stored algorithm module according to the processed training data. Training and verification are performed to obtain a machine learning model and a verification indicator corresponding to the machine learning model.

The display unit 407 is configured to display the machine learning model and its corresponding verification indicator according to a preset display rule.

Specifically, in an embodiment, the display unit 407 is configured to display the identification information of the machine learning model and the verification indicator corresponding to the machine learning model according to the size of the verification indicator.

In an embodiment, the display unit 407 can also display the data processing module identification information and the algorithm module identification information corresponding to each machine learning model, so that the user can better understand the data used in the process of generating the machine learning model. Processing methods and information such as algorithms used in model training. Of course, the display unit 407 can also display the algorithm parameters corresponding to the pre-stored algorithm modules in each machine learning model finally obtained, and the display content is not limited herein.

It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of the description, the above-mentioned machine learning model generating apparatus 400 and the specific working process of each unit can refer to the foregoing machine learning model generating method. Corresponding processes in the embodiments are not described herein again.

In the embodiment, the machine learning model generating apparatus 400 can improve the efficiency of acquiring the machine learning model, and at the same time, make the machine learning model acquisition manner more simple and convenient.

The apparatus for generating a machine learning model described above can be implemented in the form of a computer program that can be run on a computer device as shown in FIG. Please refer to FIG. 8. FIG. 8 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 can be a terminal. The terminal can be an electronic device such as a tablet computer, a notebook computer, a desktop computer, or a personal digital assistant. Referring to FIG. 8, the computer device 500 includes a processor 502, a memory, and a network interface 505 connected by a system bus 501, wherein the memory can include a non-volatile storage medium 503 and an internal memory 504.

The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a method of generating a machine learning model. The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500. The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503, which when executed by the processor 502, may cause the processor 502 to perform a method of generating a machine learning model. The network interface 505 is used for network communication, such as sending assigned tasks and the like. It will be understood by those skilled in the art that the structure shown in FIG. 8 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation of the computer device 500 to which the solution of the present application is applied, and a specific computer device. 500 may include more or fewer components than shown, or some components may be combined, or have different component arrangements.

The processor 502 is configured to execute a computer program 5032 stored in a memory to implement a method for generating a machine learning model in various embodiments of the present application. It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit, and may also be other general-purpose processors, digital signal processors, application specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, and discrete gates. Or transistor logic devices, discrete hardware components, and so on. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

In another embodiment of the present application, a storage medium is provided. The storage medium can be a computer readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. The program instructions, when executed by the processor, cause the processor to perform the method of generating the machine learning model of the present application. The storage medium may be a medium that can store program codes, such as a USB flash drive, a removable hard disk, a Read-Only Memory (ROM), a flash memory, a magnetic disk, or an optical disk.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both, for clarity of hardware and software. Interchangeability, the composition and steps of the various examples have been generally described in terms of function in the above description.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented. The steps in the method of the embodiment of the present application may be sequentially adjusted, merged, and deleted according to actual needs. The units in the apparatus of the embodiment of the present application may be combined, divided, and deleted according to actual needs. In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

The integrated unit can be stored in a storage medium if it is implemented in the form of a software functional unit and sold or used as a standalone product. Based on such understanding, the technical solution of the present application may be in essence or part of the contribution to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium. There are a number of instructions for causing a computer device (which may be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present application.

The foregoing is only a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto, and any equivalents can be easily conceived by those skilled in the art within the technical scope disclosed in the present application. Modifications or substitutions are intended to be included within the scope of the present application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

Claims (20)

1. A method for generating a machine learning model, comprising:

   Obtain training data;

   Retrieving a pre-stored data processing module and a pre-stored algorithm module;

   Performing data preprocessing on the training data according to the pre-stored data processing module to obtain processed training data;

   Performing training and verification on the pre-stored algorithm module according to the processed training data to obtain a verification index corresponding to the machine learning model and the machine learning model;

   The machine learning model and its corresponding verification indicator are displayed according to a preset display rule.
2. The method for generating a machine learning model according to claim 1, wherein before the retrieving the pre-stored data processing module and the pre-stored algorithm module, the method further comprises: acquiring data processing module identification information and algorithm module identification information;

   The retrieving the pre-stored data processing module and the pre-stored algorithm module includes:

   And corresponding to the pre-stored data processing module according to the data processing module identification information;

   And corresponding to the pre-stored algorithm module according to the algorithm module identification information.
3. The method for generating a machine learning model according to claim 2, wherein the obtaining the data processing module identification information and the algorithm module identification information comprises: acquiring the pre-stored data processing module identification information in the terminal as the data processing module identification information, and acquiring The algorithm module identification information is pre-stored in the terminal as the algorithm module identification information.
4. The method for generating a machine learning model according to claim 2, wherein the obtaining the data processing module identification information and the algorithm module identification information comprises: acquiring data processing module identification information and algorithm module identification information input by the user.
5. The method for generating a machine learning model according to claim 2, wherein the number of the data processing module identification information and the algorithm module identification information are at least one; before the retrieving the pre-stored data processing module and the pre-stored algorithm module ,Also includes:

   Arranging all of the data processing module identification information and all of the algorithm module identification information into at least one set of machine learning groups;

   The data processing module identification information and the algorithm module identification information in each group of the machine learning group are sequentially read.
6. The method for generating a machine learning model according to claim 4, wherein the obtaining the data processing module identification information and the algorithm module identification information further comprises: acquiring an algorithm parameter corresponding to the algorithm module identification information;

   And performing the training and verification on the pre-stored algorithm module according to the processed training data to obtain the machine learning model and the verification indicator corresponding to the machine learning model, including: setting, according to the processed training data, The algorithm parameters train and verify the pre-stored algorithm module to obtain a machine learning model and a verification indicator corresponding to the machine learning model.
7. The method for generating a machine learning model according to claim 1, wherein said training and verifying said pre-stored algorithm module according to said processed training data to obtain verification corresponding to said machine learning model and said machine learning model Indicators, including:

   And dividing the processed training data into training model data and verification model data according to a preset ratio;

   Training the pre-stored algorithm module to obtain a machine learning model according to the training model data;

   And verifying the machine learning model according to the verification model data to obtain a verification indicator corresponding to the machine learning model.
8. The method for generating a machine learning model according to claim 1, wherein the displaying the machine learning model and the corresponding verification indicator according to the preset display rule comprises: displaying the machine according to the size of the verification indicator The identification information of the learning model and the verification indicator corresponding to the machine learning model.
9. A device for generating a machine learning model, comprising:

   a data acquisition unit, configured to acquire training data;

   a module retrieval unit, configured to retrieve a pre-stored data processing module and a pre-stored algorithm module;

   a data processing unit, configured to perform data preprocessing on the training data according to the pre-stored data processing module to obtain processed training data;

   a model generating unit, configured to perform training and verification on the pre-stored algorithm module according to the processed training data to obtain a machine learning model and a verification indicator corresponding to the machine learning model;

   And a display unit, configured to display the machine learning model and its corresponding verification indicator according to a preset display rule.
10. A computer device comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein the processor executes the computer program: acquiring training data; a pre-stored data processing module and a pre-stored algorithm module; performing data pre-processing on the training data according to the pre-stored data processing module to obtain processed training data; and training the pre-stored algorithm module according to the processed training data Verifying to obtain a machine learning model and a verification indicator corresponding to the machine learning model; and displaying the machine learning model and its corresponding verification indicator according to a preset display rule.
11. The computer device according to claim 10, wherein before the processor executes the retrieving the pre-stored data processing module and the pre-stored algorithm module, the method further comprises: acquiring data processing module identification information and algorithm module identification information; When the pre-stored data processing module and the pre-stored algorithm module are taken, the specific implementation: the corresponding pre-stored data processing module is retrieved according to the data processing module identification information; and the corresponding pre-stored algorithm module is retrieved according to the algorithm module identification information.
12. The computer device according to claim 11, wherein the processor performs the acquisition of the data processing module identification information and the algorithm module identification information, and specifically implements: acquiring the pre-stored data processing module identification information in the terminal as the data processing module identification information, and Obtaining the pre-stored algorithm module identification information in the terminal as the algorithm module identification information.
13. The computer device according to claim 11, wherein the processor performs the acquisition of the data processing module identification information and the algorithm module identification information, and specifically implements: acquiring data processing module identification information and algorithm module identification information input by the user.
14. The computer device according to claim 11, wherein the number of the data processing module identification information and the algorithm module identification information are at least one; before the processor executes the pre-stored data processing module and the pre-stored algorithm module, Implementation: arranging all the data processing module identification information and all the algorithm module identification information into at least one set of machine learning groups; and sequentially reading data identification module identification information and algorithms in each group of the machine learning groups Module identification information.
15. The computer device according to claim 13, wherein when the processor performs the acquisition of the data processing module identification information and the algorithm module identification information, the method further comprises: acquiring an algorithm parameter corresponding to the algorithm module identification information; When the pre-stored algorithm module is trained and verified according to the processed training data to obtain the machine learning model and the verification indicator corresponding to the machine learning model, the specific implementation is: according to the processed training data, by setting the The algorithm parameters train and verify the pre-stored algorithm module to obtain a machine learning model and a verification indicator corresponding to the machine learning model.
16. A storage medium, wherein the storage medium stores a computer program, the computer program comprising program instructions that, when executed by a processor, cause the processor to execute: acquire training data; and retrieve pre-stored data processing a module and a pre-stored algorithm module; performing data pre-processing on the training data according to the pre-stored data processing module to obtain processed training data; and training and verifying the pre-stored algorithm module according to the processed training data to obtain a machine learning model and a verification indicator corresponding to the machine learning model; and displaying the machine learning model and its corresponding verification indicator according to a preset display rule.
17. The storage medium according to claim 16, wherein the program instructions, when executed by the processor to retrieve the pre-stored data processing module and the pre-stored algorithm module, further cause the processor to execute: acquiring data processing module identification information and an algorithm module The program instruction is executed by the processor when the processor executes the retrieving the pre-stored data processing module and the pre-stored algorithm module: the corresponding pre-stored data processing module is retrieved according to the data processing module identification information; The algorithm module identification information retrieves a corresponding pre-stored algorithm module.
18. The storage medium according to claim 17, wherein the program instruction, when executed by the processor, acquires data processing module identification information and algorithm module identification information, causing the processor to execute: acquiring pre-stored data processing module identification information in the terminal as The data processing module identifies the information, and obtains the pre-stored algorithm module identification information in the terminal as the algorithm module identification information.
19. The storage medium according to claim 17, wherein the program instructions, when executed by the processor to acquire the data processing module identification information and the algorithm module identification information, cause the processor to execute: acquiring data processing module identification information input by the user and Algorithm module identification information.
20. The storage medium according to claim 17, wherein the number of the data processing module identification information and the algorithm module identification information are at least one; the program instructions are executed by the processor to retrieve the pre-stored data processing module and the pre-stored algorithm. Before the module, the processor is further configured to: arrange all of the data processing module identification information and all the algorithm module identification information into at least one set of machine learning groups; and sequentially read each group of the machine learning group Data processing module identification information and algorithm module identification information.

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