# US 2021/0279562 A1 · Standardizing disparate data points

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- Number: US 2021/0279562 A1
- Document ID: US20210279562A1
- Status: Published application
- Kind: A1
- Application number: 16/808,428
- Filed: 2020-03-04
- Published: 2021-09-09
- Earliest claimed: 2020-03-04
- Assignees: International Business Machines Corporation
- Applicants: International Business Machines Corporation
- Inventors: Lukasz G. Cmielowski; Maksymilian Erazmus; Rafal Bigaj; Wojciech Sobala
- Web page: https://patentgrep.com/patent/US20210279562A1

## CPC

- G06N 3/08: Learning methods
- G06F 11/3688: {for test execution, e.g. scheduling of test suites}
- G06F 16/9538: Presentation of query results
- G06N 3/04: Architecture, e.g. interconnection topology
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/09: Supervised learning
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- G06N 20/20: Ensemble learning
- G06F 11/3447: {Performance evaluation by modeling}
- G06F 11/3616: {using software metrics}
- G06F 16/137: {Hash-based (content-based indexing of textual data G06F16/31)}
- G06F 16/2365: {Ensuring data consistency and integrity}
- G06F 16/335: Filtering based on additional data, e.g. user or group profiles (filtering in web context G06F16/9535, G06F16/9536)
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/045: Combinations of networks
- G06N 3/048: Activation functions
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

In an approach to deploying a test input based on standardized disparate data points, one or more computer processors perform one or more tests on a test input resulting in one or more disparate data points that are either non-binary or binary. The one or more computer processors standardize the one or more disparate data points utilizing a trained binary classification model. The one or more computer processors generate one or more non-binary machine learning evaluation metrics based on the one or more standardized disparate data points. The one or more computer processors, responsive to the one or more generated non-binary machine learning evaluation metrics exceeding one or more thresholds, deploy the test input.

## Claims

### Claim 1

A computer-implemented method comprising:
performing, by one or more computer processors, one or more tests on a test input resulting in one or more disparate data points that are either non-binary or binary;
standardizing, by one or more computer processors, the one or more disparate data points utilizing a trained binary classification model;
generating, by one or more computer processors, one or more non-binary machine learning evaluation metrics based on the one or more standardized disparate data points; and
responsive to the one or more generated non-binary machine learning evaluation metrics exceeding one or more thresholds, deploying, by one or more computer processors, the test input.

### Claim 2 (depends on 1)

The method of claim 1, wherein standardizing the one or more disparate data points utilizing the trained binary classification model, comprises:
classifying, by one or more computer processors, one or more non-binary disparate data points according to a binary classification technique and the trained binary classification model.

### Claim 3 (depends on 1)

The method of claim 1, wherein the non-binary machine learning evaluation metrics include at least one metric selected from the group consisting of precision, recall, accuracy, precision by label, recall by label, F1-measure by label, hamming loss, subset accuracy, F1 measure, micro precision, micro recall, and micro F1 measure.

### Claim 4 (depends on 1)

The method of claim 1, further comprising:
generating, by one or more computer processors, a report containing the generated non-binary machine learning evaluation metrics.

### Claim 5 (depends on 4)

The method of claim 4, further comprising:
notifying, by one or more computer processors, one or more users with the generated report.

### Claim 6 (depends on 4)

The method of claim 4, further comprising:
presenting, by one or more computer processors, the generated report on a web user graphical interface.

### Claim 7 (depends on 4)

The method of claim 4, further comprising:
adjusting, by one or more computer processors, one or more stylistic elements associated with the generated report.

### Claim 8 (depends on 1)

The method of claim 1, wherein the test input is a software.

### Claim 9 (depends on 8)

The method of claim 8, wherein the software contains one or more deep learning models.

### Claim 10

A computer program product comprising:
one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the stored program instructions comprising:
program instructions to perform one or more tests on a test input resulting in one or more disparate data points that are either non-binary or binary;
program instructions to standardize the one or more disparate data points utilizing a trained binary classification model;
program instructions to generate one or more non-binary machine learning evaluation metrics based on the one or more standardized disparate data points; and
program instructions to, responsive to the one or more generated non-binary machine learning evaluation metrics exceeding one or more thresholds, deploy the test input.

### Claim 11 (depends on 10)

The computer program product of claim 10, wherein the program instructions, to standardize the one or more disparate data points utilizing the trained binary classification model, comprise:
program instructions to classify one or more non-binary disparate data points according to a binary classification technique and the trained binary classification model.

### Claim 12 (depends on 10)

The computer program product of claim 10, wherein the non-binary machine learning evaluation metrics include at least one metric selected from the group consisting of precision, recall, accuracy, precision by label, recall by label, F1-measure by label, hamming loss, subset accuracy, F1 measure, micro precision, micro recall, and micro F1 measure.

### Claim 13 (depends on 10)

The computer program product of claim 10, wherein the program instructions, stored on the one or more computer readable storage media, comprise:
program instructions to generate a report containing the generated non-binary machine learning evaluation metrics.

### Claim 14 (depends on 13)

The computer program product of claim 13, wherein the program instructions, stored on the one or more computer readable storage media, comprise:
program instructions to notify one or more users with the generated report.

### Claim 15 (depends on 14)

The computer program product of claim 14, wherein the program instructions, stored on the one or more computer readable storage media, comprise:
program instructions to present the generated report on a web user graphical interface.

### Claim 16

A computer system comprising:
one or more computer processors;
one or more computer readable storage media; and
program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the stored program instructions comprising:
program instructions to perform one or more tests on a test input resulting in one or more disparate data points that are either non-binary or binary;
program instructions to standardize the one or more disparate data points utilizing a trained binary classification model;
program instructions to generate one or more non-binary machine learning evaluation metrics based on the one or more standardized disparate data points; and
program instructions to, responsive to the one or more generated non-binary machine learning evaluation metrics exceeding one or more thresholds, deploy the test input.

### Claim 17 (depends on 16)

The computer system of claim 16, wherein the program instructions, to standardize the one or more disparate data points utilizing the trained binary classification model, comprise:
program instructions to classify one or more non-binary disparate data points according to a binary classification technique and the trained binary classification model.

### Claim 18 (depends on 16)

The computer system of claim 16, wherein the non-binary machine learning evaluation metrics include at least one metric selected from the group consisting of precision, recall, accuracy, precision by label, recall by label, F1-measure by label, hamming loss, subset accuracy, F1 measure, micro precision, micro recall, and micro F1 measure.

### Claim 19 (depends on 16)

The computer system of claim 16, wherein the program instructions, stored on the one or more computer readable storage media, comprise:
program instructions to generate a report containing the generated non-binary machine learning evaluation metrics.

### Claim 20 (depends on 19)

The computer system of claim 19, wherein the program instructions, stored on the one or more computer readable storage media, comprise:
program instructions to notify one or more users with the generated report.