CN109800048A - Result methods of exhibiting, computer readable storage medium and the computer equipment of model - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000001747 exhibiting effect Effects 0.000 title claims abstract description 27
- 238000010801 machine learning Methods 0.000 claims abstract description 102
- 230000000052 comparative effect Effects 0.000 claims abstract description 51
- 230000004044 response Effects 0.000 claims abstract description 10
- 238000010586 diagram Methods 0.000 claims description 43
- 238000012360 testing method Methods 0.000 claims description 6
- 238000013480 data collection Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 abstract description 6
- 238000011156 evaluation Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 4
- 235000013399 edible fruits Nutrition 0.000 description 3
- 238000012549 training Methods 0.000 description 3
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- 238000001276 Kolmogorov–Smirnov test Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
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Abstract
The present invention provides a kind of Comparative result methods of exhibiting of machine learning model.The Comparative result methods of exhibiting includes: that the types of models selected in response to user shows multiple machine learning models and model index corresponding with each machine learning model under the types of models selected；The model comparison instruction of machine learning model and selection in response to user's selection shows the comparative result figure of the machine learning model of user's selection.The present invention compares the result of the different machines learning model trained by the same data set, and the difference between different machines learning model is shown in a manner of visual.The present invention can support the comparison between multiple model results, by the parameter information of each model, significant variable information and model error assessment result information are graphically shown, visual pattern is compared each model, selects optimal models so as to user's quicklook.
Description
Technical field
The invention belongs to machine learning techniques fields, in particular, being related to a kind of Comparative result exhibition of machine learning model
Show method, computer readable storage medium and computer equipment.
Background technique
As the mankind collect, storage, transmission, the ability fast lifting for handling data, social all trades and professions are had accumulated largely
Data, need effectively to analyze data, and machine learning (Machine Learning) has just been complied with the big epoch
Urgent need is widely used in the data process&analysis of all trades and professions.
As machine learning is in the extensive utilization of all trades and professions, more and more mechanisms are proposed the engineering of oneself one after another
Platform is practised, however in terms of machine learning model result displaying, the existing machine learning platform in market does not support visualization multimode
Type Comparative result, so that user can not distinguish machine learning model difference quickly to obtain ideal machine learning model.
Summary of the invention
In order to solve the above technical problems, the purpose of the present invention is to provide a kind of Comparative results of machine learning model
Methods of exhibiting, computer readable storage medium and computer equipment.
According to an aspect of the present invention, a kind of Comparative result methods of exhibiting of machine learning model is provided.The result
Comparison methods of exhibiting includes: that the types of models selected in response to user shows multiple machines under the types of models selected
Learning model and model index corresponding with each machine learning model；In response to user selection machine learning model and
The model comparison instruction of selection shows the comparative result figure of the machine learning model of user's selection.
Further, the types of models includes two disaggregated models and regression model.
Further, the model index include serial number corresponding with each machine learning model, model name, algorithm,
Significant variable number, Receiver operating curve, Kolmogorov-Smirnove test, promotes song at automatic configuration
Line, progress, creation time and operation.
Further, the comparative result figure includes: that model parameter comparison diagram, significant variable rank graph, model error are commented
The result for estimating comparative result figure, model index comparison diagram and corresponding case refers specifically to mark on a map.
Further, the model parameter comparison diagram includes: model name and algorithm corresponding with model name, prediction
Variable, target variable, significant variable number, optimal models tree number, measurement standard, each iteration are deleted the maximum quantity of tree, are lost
Random seed, L1 regularization when abandoning rate, discarding, L2 regularization, maximal tree depth, the probability for skipping discarding, each tree are maximum
Uniformly whether number of nodes, subsample sampling frequency, non-equilibrium data collection determine,.
Further, the significant variable rank graph include each machine learning model title and with each machine learning model
The ranking sequence of the corresponding weight variable of title.
Further, the model error assessment result comparison diagram includes multiple model error types and the mould with selection
The corresponding model error comparison diagram of type error pattern.
Further, the model index comparison diagram includes multiple model pointer types and the model index class with selection
The corresponding model index comparison diagram of type.
According to another aspect of the present invention, a kind of computer readable storage medium is additionally provided, it is described computer-readable to deposit
The Comparative result presentation program of machine learning model is stored on storage media, the Comparative result presentation program is executed by processor
The Comparative result methods of exhibiting of Shi Shixian machine learning model as described above.
According to another aspect of the invention, and provide a kind of computer equipment, the computer equipment include memory,
Processor and the Comparative result presentation program for storing the machine learning model that can be run on a memory and on a processor, it is described
Comparative result presentation program realizes the Comparative result methods of exhibiting of machine learning model as described above when being executed by processor.
Beneficial effects of the present invention: the present invention is by the result of the different machines learning model trained by the same data set
It compares, and shows the difference between different machines learning model in a manner of visual.The present invention can support multiple moulds
Comparison between type result, by the parameter information of each model, significant variable information and model error assessment result information with
The mode of chart is shown, and visual pattern is compared each model, selects optimal mould so as to user's quicklook
Type.
Detailed description of the invention
What is carried out in conjunction with the accompanying drawings is described below, above and other aspect, features and advantages of the embodiment of the present invention
It will become clearer, in attached drawing:
Fig. 1 is the flow chart of the Comparative result methods of exhibiting of the machine learning model of embodiment according to the present invention；
Fig. 2 be embodiment according to the present invention multiple machine learning models under the types of models selected and with
The display diagram of the corresponding model index of each machine learning model；
Fig. 3 is the schematic diagram of the model parameter comparison diagram of embodiment according to the present invention；
Fig. 4 is the schematic diagram of the significant variable rank graph of embodiment according to the present invention；
Fig. 5 A~Fig. 5 C is the model error assessment result comparison of each machine learning model of embodiment according to the present invention
Figure；
Fig. 6 A~Fig. 6 D is the model index comparison diagram of each machine learning model of embodiment according to the present invention；
Fig. 7 is that each machine learning model of embodiment according to the present invention corresponds to the model result of each case and refers specifically to mark on a map.
Specific embodiment
Hereinafter, with reference to the accompanying drawings to detailed description of the present invention specific embodiment.However, it is possible in many different forms
Implement the present invention, and the present invention should not be construed as limited to the specific embodiment illustrated here.On the contrary, providing these
Embodiment is in order to explain the principle of the present invention and its practical application, to make others skilled in the art it will be appreciated that originally
The various embodiments of invention and the various modifications for being suitable for specific intended application.
Fig. 1 is the flow chart of the Comparative result methods of exhibiting of the machine learning model of embodiment according to the present invention.
Referring to Fig.1, the Comparative result methods of exhibiting of the machine learning model of embodiment according to the present invention includes step
S110 and step S120.
Specifically, in step s 110, it shows in response to the types of models of user's selection in the types of models selected
Under multiple machine learning models and model index corresponding with each machine learning model.
Fig. 2 be embodiment according to the present invention multiple machine learning models under the types of models selected and with
The display diagram of the corresponding model index of each machine learning model.
Referring to Fig. 2, in the present embodiment, the difference of the function and purpose realized according to machine learning model, model class
Type may include two disaggregated models and regression model, but the present invention is not restricted to this.
Further, each machine learning model is to be trained based on same data set, but the present invention is not restricted to
This.
In addition, the model index of each machine learning model includes serial number corresponding with each machine learning model, model
Title, algorithm, significant variable number, automatic configuration, Receiver operating curve, Andrei Kolmogorov-Vladimir Smirnov inspection
It tests, lifting curve, progress, creation time and operation.Wherein, operation includes saving icon and deleting icon, is protected according to selection
Deposit icon and corresponding machine learning model saved, and according to selection delete icon and by corresponding machine learning model
It deletes, but the present invention is not restricted to this.The training progress of progress expression machine learning model.Algorithmic notation establishes machine learning
Machine learning algorithm used by model.Significant variable number refers to establishing the number of the important characteristic variable of machine learning model
Amount.
Here, model index include it is various types of be only a kind of example, therefore can according to actual needs and increase or
Reduce type.
In addition, as another embodiment of the present invention, the machine learning model and choosing that can also be selected according to user
The batch selected deletes instruction (batch as shown in Figure 2 deletes icon) and deletes the machine learning model of selection.For example, in Fig. 2
In, at least two machine learning models can be chosen, then selection batch deletes instruction, thus at least two machines that will be chosen
Learning model is deleted.
In the step s 120, the machine learning model in response to user's selection and model comparison instruction (such as Fig. 2 of selection
Shown in model compare icon) show user selection machine learning model comparative result figure.
Here, for the multiple machine learning models shown in step S110, in the step s 120, user be can choose
At least two in multiple machine learning models.In the present embodiment, in the step s 120, it is shown in step S110 multiple
Machine learning model is selected.
In the present embodiment, the comparative result figure of the machine learning model of user's selection includes: model parameter comparison diagram, again
The result of variable rank graph, model error assessment result comparison diagram, model index comparison diagram and corresponding case is wanted to refer specifically to mark on a map.This
In, all types of figures that the comparative result figure of the machine learning model of user's selection includes are only a kind of example, can be according to reality
Border increase in demand or reduction.
Fig. 3 is the schematic diagram of the model parameter comparison diagram of embodiment according to the present invention.
Referring to Fig. 3, the model parameter comparison diagram of embodiment according to the present invention includes: model name and and model name
Corresponding algorithm, predictive variable, target variable, significant variable number, optimal models tree number, measurement standard, each iteration are deleted
The maximum quantity of tree, random seed when abandoning, L1 regularization, L2 regularization, maximal tree depth, skips discarding at loss ratio
Uniformly whether probability, each tree maximum node number, subsample sampling frequency, non-equilibrium data collection determine,.Here, the present embodiment
Model parameter comparison diagram in each parameter for enumerating and number of parameters be only a kind of example, can increase according to actual needs
Or it reduces.
Fig. 4 is the schematic diagram of the significant variable rank graph of embodiment according to the present invention.
Referring to Fig. 4, the significant variable rank graph of embodiment according to the present invention include: each machine learning model title and
The ranking sequence of weight variable corresponding with each machine learning model title.Here, significant variable refers to establishing machine learning
The important characteristic variable of model.The successive ranking of significant variable is the weight degree according to significant variable in machine learning model
And determine.Here, the significant variable rank graph of the present embodiment is only a kind of example, the quantity of machine learning model therein
And the quantity of significant variable can increase or decrease according to actual needs.
Fig. 5 A~Fig. 5 C is the model error assessment result comparison of each machine learning model of embodiment according to the present invention
Figure.
Referring to Fig. 5 A~Fig. 5 C, the model error assessment result comparison diagram of embodiment according to the present invention includes: multiple moulds
Type error pattern and model error assessment result comparison diagram corresponding with the model error type of selection.That is, according to
One of different model error types of selection, such as selection ROC icon, K-S icon and LIFT icon, show the mould with selection
The corresponding model error assessment result comparison diagram of type error pattern.Further, width model error assessment knot can be only shown
Fruit comparison diagram, different (such as one of selection ROC icon, K-S icon and LIFT icon) according to the icon of selection, change is chosen
The corresponding model error assessment result of icon comparison diagram.
In the present embodiment, it such as selects ROC icon (indicating the ROC error of model), shows each shown in Fig. 5 A
The Receiver Operating Characteristics of machine learning model (such as Fig. 5 A show model 0, model 1, model 2, model 3 and model 4) are bent
The error evaluation comparative result figure of line (Receiver Operating Characteristic Curve, abbreviation ROC curve).
Such as selection K-S icon (indicating the K-S error of model), show each machine learning model shown in Fig. 5 B
The Kolmogorov-Smirnove test of (such as Fig. 5 B show model 0, model 1, model 2, model 3 and model 4)
The error evaluation comparative result figure of (Kolmogorov-Smirnov Test, abbreviation K-S are examined).
Such as selection LIFT icon (indicating the promotion error of model), show each machine learning model shown in Fig. 5 C
The error evaluation result pair of the promotion (i.e. Lift) of (such as Fig. 5 B show model 0, model 1, model 2, model 3 and model 4)
Than figure.
In addition, it is necessary to explanation, in Fig. 5 A~Fig. 5 C, what the figure in the left side in every width figure indicated is according to training sample
Originally the error evaluation comparative result figure obtained；And what the figure on the right side in every width figure indicated is the error obtained according to test samples
Assessment result comparison diagram.
In addition, the Comparative result methods of exhibiting of embodiment according to the present invention further include: in response to the model of user's selection
Error pattern shows error evaluation comparative result figure of each machine learning model under the error pattern that user selects.In addition,
It in Fig. 5 A~Fig. 5 C, selectes the figure in the left side in every width figure, can show each machine learning model (according to training sample)
Error evaluation end value.Correspondingly, in the figure for selecting the right side in every width figure, each machine learning model also can all be shown
The error evaluation end value of (according to test samples).
Fig. 6 A~Fig. 6 D is the model index comparison diagram of each machine learning model of embodiment according to the present invention.
Referring to Fig. 6 A~Fig. 6 D, the model index comparison diagram of embodiment according to the present invention includes: multiple machine learning moulds
Type pointer type and model index comparison diagram corresponding with the machine learning model pointer type of selection.That is, according to
The different model pointer types of selection, such as selection ROC icon, promotion icon, accumulative one of icon and income icon, show
Model index comparison diagram corresponding with the model pointer type of selection.Further, it can only show that a width model index compares
Figure, it is different (such as selection ROC icon, promotion icon, accumulative one of icon and income icon) according to the icon of selection, change quilt
The corresponding model index comparison diagram of selected icon.
In the present embodiment, it such as selects ROC icon (indicating the ROC index of model), shows each shown in Fig. 6 A
The Receiver Operating Characteristics of machine learning model (such as Fig. 6 A show model 0, model 1, model 2, model 3 and model 4) are bent
The model index comparison diagram of line (Receiver Operating Characteristic Curve, abbreviation ROC curve).
Such as selection promotes icon (indicating the promotion index of model), shows each machine learning model shown in Fig. 6 B
The model index comparison diagram of the promotion (LIFT) of (such as Fig. 6 B show model 0, model 1, model 2, model 3 and model 4).
Such as accumulative icon (indicating the accumulative index of model) is selected, show each machine learning model shown in Fig. 6 C
The model index comparison diagram of accumulative (i.e. the Lift) of (such as Fig. 6 C show model 0, model 1, model 2, model 3 and model 4).
Such as selection income icon (indicating the proceeds indicatior of model), show each machine learning model shown in Fig. 6 D
The model index comparison diagram of the income (i.e. Lift) of (such as Fig. 6 D show model 0, model 1, model 2, model 3 and model 4).
In addition, the Comparative result methods of exhibiting of embodiment according to the present invention further include: in response to the model of user's selection
Pointer type shows corresponding model index comparison diagram of each machine learning model under the model pointer type that user selects.
Fig. 7 is that each machine learning model of embodiment according to the present invention corresponds to the model result of each case and refers specifically to mark on a map.
Referring to Fig. 7, each machine learning model of embodiment according to the present invention corresponds to the model result specific targets of each case
It include: case (i.e. case number (CN)) in figure, number (i.e. the number of machine learning model), the concern classification in section is constituted, concern classification is tired out
Product percentage is promoted and adds up to be promoted.Here, the model result of each case of the correspondence of the present embodiment refers specifically to mark on a map only one
Kind example, the quantity of machine learning model therein and the type of specific targets can increase or decrease according to actual needs.
It should be noted that the case shown in Fig. 6 B, Fig. 6 C and Fig. 7 is defined as: by scheduled total number of samples amount
It is divided into several pieces according to scheduled allocation rule, wherein every part is defined as a case.For example, total number of samples amount is 1000, press
According to etc. a point allocation rule be divided into 10 equal portions, every part of 100 sample sizes, every part is a case, i.e., every case has 100 sample numbers
Amount.Alternatively, total number of samples amount is 1000, it is divided into 10 equal portions according to non-equal part allocation rule, every part has the sample number being set
Amount, every part is a case, i.e., every case has the sample size being set.Moreover it is preferred that by the result to machine learning model
The significant samples being affected are divided into the first case, but the present invention is not restricted to this.
In addition, showing the model knot that each machine learning model corresponds to the case number (CN) according to the case number (CN) that user selects in Fig. 7
Fruit specific targets.Such as selecting case number (CN) is 1, then show each machine learning model correspond to case number (CN) be 1 model result refer specifically to
Mark, i.e., each specific targets for the model result that each machine learning model obtains under the sample in case 1.As of the invention another
Embodiment can also show each machine learning model according to the case number (CN) that user selects and correspond to the case number (CN) and the before model of case number (CN)
As a result specific targets, i.e., each machine learning model the case number (CN) and before case number (CN) sample under obtain model result it is each specific
Index.
Another embodiment of the present invention additionally provides a kind of computer readable storage medium, the computer-readable storage medium
The Comparative result presentation program of machine learning model is stored in matter, it is real when the Comparative result presentation program is executed by processor
The Comparative result methods of exhibiting of machine learning model now as shown in Figure 1.
Another embodiment of the present invention provides a kind of computer equipment again, and the computer equipment includes memory, place
Reason device and the Comparative result presentation program for storing the machine learning model that can be run on a memory and on a processor, the knot
Fruit comparison presentation program realizes the Comparative result methods of exhibiting of machine learning model as shown in Figure 1 when being executed by processor.
In conclusion embodiment according to the present invention is by the different machines learning model trained by the same data set
As a result it compares, and shows the difference between different machines learning model in a manner of visual.It can support multiple models
As a result the comparison between, by the parameter information of each model, significant variable information and model error assessment result information are to scheme
The mode of table is shown, and visual pattern is compared each model, selects optimal models so as to user's quicklook.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that the process, method, article or the device that include a series of elements not only include those elements, and
And further include other elements that are not explicitly listed, or further include for this process, method, article or device institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do
There is also other identical elements in the process, method of element, article or device.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in a storage medium
In (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that a terminal (can be mobile phone, computer, service
Device or the network equipment etc.) execute method described in each embodiment of the present invention.
The embodiment of the present invention is described with above attached drawing, but the invention is not limited to above-mentioned specific
Embodiment, the above mentioned embodiment is only schematical, rather than restrictive, those skilled in the art
Under the inspiration of the present invention, without breaking away from the scope protected by the purposes and claims of the present invention, it can also make very much
Form, all of these belong to the protection of the present invention.
Claims (10)
1. a kind of Comparative result methods of exhibiting of machine learning model, which is characterized in that the Comparative result methods of exhibiting includes:
In response to user's selection types of models show multiple machine learning models under the types of models selected and
Model index corresponding with each machine learning model；
The model comparison instruction of machine learning model and selection in response to user's selection shows the engineering of user's selection
Practise the comparative result figure of model.
2. Comparative result methods of exhibiting according to claim 1, which is characterized in that the types of models includes two classification moulds
Type and regression model.
3. Comparative result methods of exhibiting according to claim 1 or 2, which is characterized in that the model index include with respectively
The corresponding serial number of a machine learning model, model name, algorithm, significant variable number, automatic configuration, Receiver Operating Characteristics are bent
Line, Kolmogorov-Smirnove test, lifting curve, progress, creation time and operation.
4. Comparative result methods of exhibiting according to claim 1, which is characterized in that the comparative result figure includes: model
The result of comparative bid parameter, significant variable rank graph, model error assessment result comparison diagram, model index comparison diagram and corresponding case
It refers specifically to mark on a map.
5. Comparative result methods of exhibiting according to claim 4, which is characterized in that the model parameter comparison diagram includes:
Model name and algorithm corresponding with model name, predictive variable, target variable, significant variable number, optimal models tree number,
Measurement standard, each iteration delete tree maximum quantity, loss ratio, abandon when random seed, L1 regularization, L2 regularization,
Maximal tree depth, the probability for skipping discarding, each tree maximum node number, subsample sampling frequency, non-equilibrium data collection determine, are
It is no uniform.
6. Comparative result methods of exhibiting according to claim 4, which is characterized in that the significant variable rank graph includes each
The ranking sequence of machine learning model title and weight variable corresponding with each machine learning model title.
7. Comparative result methods of exhibiting according to claim 4, which is characterized in that the model error assessment result comparison
Figure includes multiple model error types and model error comparison diagram corresponding with the model error type of selection.
8. Comparative result methods of exhibiting according to claim 4, which is characterized in that the model index comparison diagram includes more
A model pointer type and model index comparison diagram corresponding with the model pointer type of selection.
9. a kind of computer readable storage medium, which is characterized in that be stored with engineering on the computer readable storage medium
The Comparative result presentation program for practising model realizes such as claim 1 or 8 when the Comparative result presentation program is executed by processor
The Comparative result methods of exhibiting of the machine learning model.
10. a kind of computer equipment, which is characterized in that the computer equipment includes memory, processor and is stored in storage
On device and the Comparative result presentation program of machine learning model that can run on a processor, the Comparative result presentation program quilt
Processor realizes the Comparative result methods of exhibiting of machine learning model as claimed in claim 1 or 8 when executing.
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| CN111445029A (en) * | 2020-03-30 | 2020-07-24 | 北京嘉楠捷思信息技术有限公司 | Model evaluation method, model evaluation device and computer-readable storage medium |
| WO2022137573A1 (en) * | 2020-12-25 | 2022-06-30 | 富士通株式会社 | Information processing program, information processing method, and information processing device |
| CN113780287A (en) * | 2021-07-30 | 2021-12-10 | 武汉中海庭数据技术有限公司 | Optimal selection method and system for multi-depth learning model |
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