CN111144617B - Method and device for determining model - Google Patents
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- 235000008429 bread Nutrition 0.000 description 4
- 240000007124 Brassica oleracea Species 0.000 description 3
- 241000287828 Gallus gallus Species 0.000 description 3
- 240000007594 Oryza sativa Species 0.000 description 3
- 235000007164 Oryza sativa Nutrition 0.000 description 3
- 235000009566 rice Nutrition 0.000 description 3
- 206010034203 Pectus Carinatum Diseases 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
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Abstract
The application provides a method and a device for determining a model, wherein the method comprises the following steps: EI is calculated through M models to be measured respectively m The method comprises the steps of carrying out a first treatment on the surface of the According to EI m And determining a target standby model from the M models to be tested. According to the technical scheme, the more accurate model for prediction can be determined by setting the evaluation index of the model, and sales quantity prediction, goods intake quantity prediction or yield prediction and the like are more accurate through the determined model.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a method and apparatus for determining a model.
Background
In order to predict sales using machine learning algorithms, it is necessary to select an appropriate algorithm based on the actual sales data set. In selecting the algorithm, the model is trained by various algorithms, the trained model is determined, and the good index for determining the model can help to select the algorithm better.
Currently, the determination models for machine learning algorithm models fall into two main categories: for the classification algorithm, indexes such as accuracy, recall rate, F-score and the like are adopted. For the regression algorithm, indices such as RMSE (Root Mean Squared Error, root mean square error), MSE (Mean Squared Error, mean square error), MAE (Mean Absolute Error ), MAPE (Mean Absolute Percentage Error, mean absolute percentage error) and the like are used.
For a multi-output regression model, if the orders of magnitude of the outputs are different greatly, such as a number level and a percentage level, when the model is evaluated by using a traditional evaluation index, the model is usually prone to outputting of a certain order of magnitude (including orders of magnitude with a large number of outputs), and is not accurate enough for outputting of other orders of magnitude.
Disclosure of Invention
The application aims to provide a method and a device for determining a model, which can determine a more accurate model for prediction.
In order to solve the technical problem, the application provides a method for determining a model, which comprises the following steps:
EI is calculated through M models to be measured respectively m ；
According to EI m The value determines a target standby model from the M models to be tested;
wherein ,
EI i calculating an error value of the ith commodity for the mth model to be measured; q (Q) i A base for the data amount of the ith commodity;
m=1, 2 … M, i=1, 2 … N; m and N are positive integers greater than 1;
for each of the N commodities, the commodity type is the same and the characteristics of the commodity are different;
the data volume includes sales volume, shipping volume, or production volume.
Optionally, the step of determining the EI m The value determining the target standby model from the M models to be tested comprises:
EI is set m And the model to be tested with the minimum value is taken as a target standby model.
Optionally, the EI is calculated by M models to be measured m Previously, the method further comprises:
calculating the base Q of the data quantity corresponding to the ith commodity i 。
Optionally, the base Q of the data volume corresponding to the ith commodity is calculated i Comprising the following steps:
for the ith commodity, calculating the average value A of the data quantity of the commodity according to the S sample data of the commodity i And standard deviation D i ；
For the S sample data, the numerical value is greater than X i Is smaller than Y i Calculating an average value of the remaining sample data;
taking the average value of the rest sample data as the base Q of the data volume of the commodity i ；
Wherein S is a positive integer greater than or equal to 1;
X i ＝A i +2D i ；Y i ＝A i -2D i 。
optionally, the error value includes a root mean square error RMSE, a mean square error MSE, a mean absolute error MAE, a mean absolute percentage error MAPE.
The application also provides a device for determining a model, which comprises: a memory and a processor;
the memory is used for storing a program for determining a model;
the processor is configured to read and execute the program for determining a model, and perform the following operations:
EI is calculated through M models to be measured respectively m ；
According to EI m The value determines a target standby model from the M models to be tested;
wherein ,
EI i calculating an error value of the ith commodity for the mth model to be measured; q (Q) i A base for the data amount of the ith commodity;
m=1, 2 … M, i=1, 2 … N; m and N are positive integers greater than 1;
for each of the N commodities, the commodity type is the same and the characteristics of the commodity are different;
the data volume includes sales volume, shipping volume, or production volume.
Optionally, the step of determining the EI m The value determining the target standby model from the M models to be tested comprises:
EI is set m And the model to be tested with the minimum value is taken as a target standby model.
Optionally, the processor is configured to read and execute the program for determining a model, and further perform the following operations:
the EI is calculated through M models to be measured respectively m Previously, the base Q of the data quantity corresponding to the ith commodity is calculated i 。
Optionally, the base Q of the data volume corresponding to the ith commodity is calculated i Comprising the following steps:
for the ith commodity, calculating the average value A of the data quantity of the commodity according to the S sample data of the commodity i And standard deviation D i ；
For the S sample data, the numerical value is greater than X i Is smaller than Y i Calculating an average value of the remaining sample data;
taking the average value of the rest sample data as the base Q of the data volume of the commodity i ；
Wherein S is a positive integer greater than or equal to 1;
X i ＝A i +2D i ；Y i ＝A i -2D i 。
optionally, the error value includes a root mean square error RMSE, a mean square error MSE, a mean absolute error MAE, a mean absolute percentage error MAPE.
The application comprises the following steps: EI is calculated through M models to be measured respectively m The method comprises the steps of carrying out a first treatment on the surface of the According to EI m The value determines a target standby model from the M models to be tested; wherein,
EI i calculating an error value of the ith commodity for the mth model to be measured; q (Q) i A base for the data amount of the ith commodity;
m=1, 2 … M, i=1, 2 … N; m and N are positive integers greater than 1; for each of the N commodities, the commodity type is the same and the characteristics of the commodity are different; the data volume includes sales volume, shipping volume, or production volume. According to the technical scheme, the more accurate model for prediction can be determined by setting the evaluation index of the model, and sales quantity prediction, goods intake quantity prediction or yield prediction and the like are more accurate through the determined model.
Drawings
The accompanying drawings are included to provide an understanding of the principles of the application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the principles of the application.
FIG. 1 is a flow chart of a method of determining a model according to a first embodiment of the present application;
FIG. 2 is a schematic structural view of a device for determining a model according to a first embodiment of the present application;
fig. 3 is another flowchart of a method of determining a model according to a first embodiment of the present application.
Detailed Description
The present application has been described in terms of several embodiments, but the description is illustrative and not restrictive, and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the described embodiments. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or in place of any other feature or element of any other embodiment unless specifically limited.
The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The disclosed embodiments, features and elements of the present application may also be combined with any conventional features or elements to form a unique inventive arrangement as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive arrangements to form another unique inventive arrangement as defined in the claims. It is therefore to be understood that any of the features shown and/or discussed in the present application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not to be restricted except in light of the attached claims and their equivalents. Further, various modifications and changes may be made within the scope of the appended claims.
Furthermore, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other sequences of steps are possible as will be appreciated by those of ordinary skill in the art. Accordingly, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Furthermore, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present application.
Example 1
As shown in fig. 1, the present embodiment provides a method for determining a model, the method including:
step S101, respectively calculating EI through M models to be detected m ；
Step S102, according to EI m The value determines a target standby model from the M models to be tested;
wherein ,
EI i calculating an error value of the ith commodity for the mth model to be measured; q (Q) i A base for the data amount of the ith commodity;
m=1, 2 … M, i=1, 2 … N; m and N are positive integers greater than 1;
for each of the N commodities, the commodity type is the same and the characteristics of the commodity are different;
the data volume includes sales volume, shipping volume, or production volume.
In this embodiment, the N selected commodities may be similar commodities but of different types. For example, selecting N items may be N dishes: mutton steamed bread, vermicelli cabbage, tomato eggs and the like; or the selected N commodities may be N electronic products: talent watch Z2, talent watch Z3, talent watch Z5, mate 30, etc.
Optionally, the step of determining the EI m Value determination of the target standby model from the M models to be tested may include:
EI is set m And the model to be tested with the minimum value is taken as a target standby model.
Optionally, the EI is calculated by M models to be measured m Previously, the method may further comprise:
calculating the base Q of the data quantity corresponding to the ith commodity i 。
Optionally, the base Q of the data volume corresponding to the ith commodity is calculated i May include:
for the ith commodity, calculating the average value A of the data quantity of the commodity according to the S sample data of the commodity i And standard deviation D i ；
For the S sample data, the numerical value is greater than X i Is smaller than Y i Calculating an average value of the remaining sample data;
the remaining sample is takenThe average value of the data is taken as the base Q of the data volume of the commodity i ；
Wherein S is a positive integer greater than or equal to 1;
X i ＝A i +2D i ；Y i ＝A i -2D i 。
alternatively, the error values may include a root mean square error RMSE, a mean square error MSE, a mean absolute error MAE, a mean absolute percentage error MAPE.
According to the technical scheme, each model is evaluated by setting the evaluation index of the model, so that the model with more accurate prediction can be determined, the selected model is more reasonable and practical, and sales quantity prediction, stock quantity prediction or yield prediction and the like performed by the selected model are more accurate.
As shown in fig. 2, this embodiment further provides an apparatus for determining a model, where the apparatus includes: a memory 10 and a processor 11;
the memory 10 is used for storing a program for determining a model;
the processor 11 is configured to read and execute the program for determining a model, and perform the following operations:
EI is calculated through M models to be measured respectively m ；
According to EI m The value determines a target standby model from the M models to be tested;
wherein ,
EI i calculating an error value of the ith commodity for the mth model to be measured; q (Q) i A base for the data amount of the ith commodity;
m=1, 2 … M, i=1, 2 … N; m and N are positive integers greater than 1;
for each of the N commodities, the commodity type is the same and the characteristics of the commodity are different;
the data volume includes sales volume, shipping volume, or production volume.
Optionally, the step of determining the EI m Value determination of the target standby model from the M models to be tested may include:
EI is set m And the model to be tested with the minimum value is taken as a target standby model.
Optionally, the processor 11 is configured to read and execute the program for determining a model, and may further perform the following operations:
the EI is calculated through M models to be measured respectively m Previously, the base Q of the data quantity corresponding to the ith commodity is calculated i 。
Optionally, the base Q of the data volume corresponding to the ith commodity is calculated i May include:
for the ith commodity, calculating the average value A of the data quantity of the commodity according to the S sample data of the commodity i And standard deviation D i ；
For the S sample data, the numerical value is greater than X i Is smaller than Y i Calculating an average value of the remaining sample data;
taking the average value of the rest sample data as the base Q of the data volume of the commodity i ；
Wherein S is a positive integer greater than or equal to 1;
X i ＝A i +2D i ；Y i ＝A i -2D i 。
alternatively, the error values may include a root mean square error RMSE, a mean square error MSE, a mean absolute error MAE, a mean absolute percentage error MAPE.
According to the technical scheme, each model is evaluated by setting the evaluation index of the model, so that the model with more accurate prediction can be determined, the selected model is more reasonable and practical, and sales quantity prediction, stock quantity prediction or yield prediction and the like performed by the selected model are more accurate.
The method of determining a model of the present application is further illustrated by way of example below.
This example is described taking sales of dishes as an example, assuming that dishes respectively include: diced chicken, white-burned cabbage mustard, braised pork, steamed rice and steamed bread.
Currently there are 6 models to be tested for predicting sales of the 5 dishes mentioned above (i.e. m= 6;N =5).
As shown in fig. 3, the method of determining a model of the present example may include:
step S301, calculating sales quantity base numbers of 5 dishes respectively;
in this example, it is necessary to calculate the sales base of the chicken in palace, the sales base of the white leaf mustard, the sales base of the red-boiled meat, the sales base of the steamed rice, and the sales base of the steamed bread.
The calculation of the sales base for a chicken nugget may be performed as follows:
first, sample data may be collected according to the specific situation, and the sales amount may be a daily sales amount in one month, a daily sales amount in one year, or a sales amount in each month in one year.
Assuming that the 1 st commodity is a chicken breast, the daily sales of chicken breast (s=7) in one week are collected: 2. 12, 17, 19, 15, 20, 16, and calculating an average value a of the 7 sample data 1 Standard deviation D of 14.429 for these 7 sample data 1 Is 5.628, X 1 ＝A 1 +2D 1 ＝14.429+2*5.628＝25.69，X 1 ＝A 1 -2D 1 Sample data 2 is deleted by deleting sample data of more than 25.69 and sample data of less than 3.17, the remaining sample data being 12, 17, 19, 15, 20, 16, and then the average value of 12, 17, 19, 15, 20, 16 is calculated to be 16.5, so that the sales base Q of the chicken in palace is calculated 1 16.5.
Based on the above mode, calculating sales base of white-burned cabbage mustard, sales base of red-burned meat, sales base of steamed rice and sales base of steamed bread.
Step S102, respectively calculating EI through 6 models to be tested m ；
wherein ,
in this example, EI i The RMSE of the ith commodity calculated for the mth model to be measured; q (Q) i The base of sales for the ith commodity;
m＝1，2…6，i＝1，2…5。
step S103, EI m And the model to be tested with the minimum value is taken as a target standby model.
Assume that the EI calculated by the 3 rd model under test 3 And if the sales quantity is minimum, the 3 rd model to be measured is used as a target standby model, namely, the sales quantity prediction is more accurate by using the 3 rd model to be measured, and the method can be suitable for predicting the output of various orders of magnitude.
According to the technical scheme, each model is evaluated by setting the evaluation index of the model, so that the model with more accurate prediction can be determined, the determined model is more reasonable and practical, and sales quantity prediction, stock quantity prediction or yield prediction and the like performed by the determined model are more accurate.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
Claims (6)
1. A method of determining a model, the method comprising:
EI is calculated through M models to be measured respectively m ；
According to EI m The value determines a target standby model from the M models to be tested;
wherein ,
EI i calculating an error value of the ith commodity for the mth model to be measured; q (Q) i A base for the data amount of the ith commodity;
m=1, 2 … M, i=1, 2 … N; m and N are positive integers greater than 1;
for each of the N commodities, the commodity type is the same and the characteristics of the commodity are different;
the data amount includes sales amount, stock amount or yield,
the EI is calculated through M models to be measured respectively m Previously, the method further comprises:
calculating the base Q of the data quantity corresponding to the ith commodity i ；
The base Q of the data quantity corresponding to the ith commodity is calculated i Comprising the following steps:
for the ith commodity, calculating the average value A of the data quantity of the commodity according to the S sample data of the commodity i And standard deviation D i ；
For the S sample data, the numerical value is greater than X i Is smaller than Y i Calculating an average value of the remaining sample data;
taking the average value of the rest sample data as the base Q of the data volume of the commodity i ；
Wherein S is a positive integer greater than or equal to 1;
X i ＝A i +2D i ；Y i ＝A i -2D i 。
2. the method of claim 1, wherein the step of determining the difference in the EIs m The value determining the target standby model from the M models to be tested comprises:
EI is set m And the model to be tested with the minimum value is taken as a target standby model.
3. A method according to any one of claims 1 to 2, wherein:
the error values include root mean square error RMSE, mean square error MSE, mean absolute error MAE, mean absolute percentage error MAPE.
4. An apparatus for determining a model, the apparatus comprising: a memory and a processor; the method is characterized in that:
the memory is used for storing a program for determining a model;
the processor is configured to read and execute the program for determining a model, and perform the following operations:
EI is calculated through M models to be measured respectively m ；
According to EI m The value determines a target standby model from the M models to be tested;
wherein ,
EI i calculating an error value of the ith commodity for the mth model to be measured; q (Q) i A base for the data amount of the ith commodity;
m=1, 2 … M, i=1, 2 … N; m and N are positive integers greater than 1;
for each of the N commodities, the commodity type is the same and the characteristics of the commodity are different;
the data amount includes sales amount, stock amount or yield,
the processor is configured to read and execute the program for determining a model, and further perform the following operations:
the EI is calculated through M models to be measured respectively m Previously, the base Q of the data quantity corresponding to the ith commodity is calculated i ；
Calculating the base Q of the data quantity corresponding to the ith commodity i Comprising the following steps:
for the ith commodity, calculating the average value A of the data quantity of the commodity according to the S sample data of the commodity i And standard deviation D i ；
For the S sample data, the numerical value is greater than X i Is smaller than Y i Calculating an average value of the remaining sample data;
taking the average value of the rest sample data as the base Q of the data volume of the commodity i ；
Wherein S is a positive integer greater than or equal to 1;
X i ＝A i +2D i ；Y i ＝A i -2D i 。
5. the apparatus of claim 4, wherein the apparatus according to EI m The value determining the target standby model from the M models to be tested comprises:
EI is set m And the model to be tested with the minimum value is taken as a target standby model.
6. The apparatus of any one of claims 4 to 5, wherein:
the error values include root mean square error RMSE, mean square error MSE, mean absolute error MAE, mean absolute percentage error MAPE.
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