영어에서 Mining model 을 사용하는 예와 한국어로 번역
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This class defines the mining model flag.
이 플래그는 마이닝 모델 열에 정의됩니다.A data mining model is an empty object until it is processed.
데이터 마이닝 모델은 처리되기 전까지는 비어 있는 개체입니다.A data mining model is an empty object until it is processed.These patterns and trends can be collected and defined as a data mining model.
이러한 패턴과 트랜드를 수집하여 데이터 마이닝 모델로 정의할 수 있다.Mining Model Content for Decision Tree Models(Analysis Services- Data Mining). .
의사 결정 트리 모델에 대한 마이닝 모델 콘텐츠(Analysis Services - 데이터 마이닝)Mining Model Content for Decision Tree Models (Analysis Services - Data Mining).How to: Change the Discretization of a Column in a Mining Model.
마이닝 모델에서 열의 분할 변경Change the Discretization of a Column in a Mining Model.The mining model contains a set of bindings, which point back to the data stored in the mining structure.
모델은 마이닝 구조에 캐시된 데이터를 다시 가리키는 바인딩 집합을 포함합니다. The model does contain a set of bindings, which point back to the data cached in the mining structure.The algorithm uses the results of this analysis to define the optimal parameters for creating the mining model.
알고리즘은 많은 반복을 통해 이 분석 결과를 사용하여 마이닝 모델을 만들기 위한 최적의 매개 변수를 찾습니다.A mining model is empty until the data provided by the mining structure has been processed and analyzed.
마이닝 모델은 마이닝 구조에서 제공한 데이터가 처리 및 분석되기 전까지 비어 있습니다.A mining model is empty until the data provided by the mining structure has been processed and analyzed.Setting the CacheMode property to ClearAfterProcessing will disable drillthrough from the mining model.
CacheMode 속성을 ClearAfterProcessing 으로 설정하면 마이닝 모델에서 드릴스루 기능을 사용할 수 없게 됩니다.A data mining model applies a mining model algorithm to the data that is represented by a mining structure.
데이터 마이닝 모델은 마이닝 구조가 나타나는 데이터에 For more information, see How to: Change the Discretization of a Column in a Mining Model.
For information about how to do this, see Change the Discretization of a Column in a Mining Model.This is because the structure of nodes in the mining model does not necessarily correspond directly to the underlying data.
이는 마이닝 모델의 노드 구조가 기본 데이터와 반드시 일치하지는 않기 때문입니다. This is because the structure of the nodes in the mining model does not necessarily correspond directly to the underlying data.These patterns and trends can be collected and defined as a data mining model.
이러한 패턴과 추세를 수집하여 데이터 마이닝 모델로 정의할 수 있습니다. These patterns and trends can be collected and defined as a data mining model.The mining model contains the results of the mining process- along with the metadata and bindings back to the mining structures.
마이닝 모델이 처리된 후에는 메타데이터, 결과 및 For more information, see How to: Change the Discretization of a Column in a Mining Model.
자세한 내용은 마이닝 모델에서 열의 불연속화 변경을 참조하세요. For more information, Change the Discretization of a Column in a Mining Model.Each mining model can also have properties that are derived from the mining structure, and that describe the columns of data used by the model. .
각 마이닝 모델에는 마이닝 구조에서 파생되었으며 해당 모델에서 사용하는 데이터의 열을 설명하는 속성도 있습니다. Each mining model also has properties that are derived from the mining structure, and that describe the columns of data used by the model.It is importantto remember that whenever the data changes, you must update both the mining structure and the mining model.
It is important to remember that whenever the data changes,you must update both the The wizard is quick and easy, and guides you through the process of creating a data mining structure and an initial related mining model, and includes the tasks of selecting an algorithm type and a data source, and defining the case data used for analysis.
이 마법사는 빠르고 쉬우며 데이터 The Data Mining wizard walks you through the process of creating a mining structure,choosing data, and adding a mining model.
데이터 마이닝 마법사는 마이닝 구조를 만들고 데이터를 선택하고 마이닝 모델을 추가하는 과정을 안내해 줍니다. The Data A mining model is an object that belongs to a particular mining structure, and the model inherits all the values of the properties that are defined by the mining structure.
마이닝 모델은 마이닝 구조 내에 포함되며 Add new models to an existing structure; copy models, change model properties or metadata, or define filters on a mining model.
기존 구조에 새 모델을 추가하고, 모델을 복사하고, 모델 속성이나 메타데이터를 변경하고, 마이닝 모델에 대한 필터를 정의합니다. Add new models to an existing structure; copy models, change A mining model is an object that belongs to a particular mining structure, and the model inherits all the values of the properties that are defined by the mining structure.
마이닝 모델은 특정 Building Models The fourth step in the data mining process,as highlighted in the following diagram, is to build the mining model or models. .
데이터 마이닝 프로세스의 4번째 단계는 다음 다이어그램에 강조 표시된 바와 같이 마이닝 모델을 작성하는 것입니다. The fourth step in the data If you create a mining model by using Data Mining Extensions(DMX), you can specify the model and the columns in it, and DMX will automatically create the required mining structure.
DMX(Data Mining Extensions)를 사용하여 마이닝 모델을 만드는 경우 모델과 해당 모델의 열을 지정할 수 있으며 DMX에서는 필요한 Data flagged in this way can still be used in queries if drillthrough has been enabled on the mining model, and if you have the appropriate permissions.
마이닝 모델에 드릴스루가 사용되도록 설정되어 있고 적절한 권한이 있을 경우 이와 같이 플래그가 지정된 데이터도 쿼리에 사용될 수 있습니다. Data flagged in this way can still be used in queries if drillthrough has been enabled on the mining model, and if you have the appropriate permissions.For example, a data mining model that correlates store location with sales might be both accurate and reliable, but might not be useful, because you cannot generalize that result by adding more stores at the same location.
예를 들어 매장 위치와 판매량 간 상관 관계를 찾는 데이터 마이닝 모델은 정확하면서 안정적일 수 있지만 동일한 위치에 있는 다른 매장을 추가하여 결과를 일반화할 수 없으므로 유용하지 않을 수 있습니다. For example, a data mining model that correlates store location with sales might be both accurate and reliable, but might not be useful, because you cannot generalize that result by adding more stores at the same location.The wizard is quick and easy, and guides you through the process of creating a data mining structure and an initial related mining model, and includes the tasks of selecting an algorithm type and a data source, and defining the case data used for analysis.+ For More Information: Data Mining Wizard(Analysis Services- Data Mining). .
이 마법사는 빠르고 쉬우며 데이터 A mining model is created by applying an algorithm to data, but it is more than an algorithm or a metadata container: it is a set of data, statistics, and patterns that can be applied to new data to generate predictions and make inferences about relationships.
마이닝 모델 은 데이터에 알고리즘을 적용하여 만들지만 단순한 알고리즘 또는 메타데이터 컨테이너가 아니며, 새로운 데이터에 적용하여 예측을 생성하고 관계를 추론할 수 있는 데이터, 통계 및 패턴의 집합입니다.A mining model is created by applying an algorithm to data, but it is more than an algorithm or a metadata container: it is a set of data, statistics, and patterns that can be applied to new data to generate predictions and make inferences about relationships.APPLIES TO: SQL Server Analysis Services Azure Analysis Services A mining model is created by applying an algorithm to data, but it is more than an algorithm or a metadata container: it is a set of data, statistics, and patterns that can be applied to new data to generate predictions.
마이닝 모델 은 데이터에 알고리즘을 적용하여 만들지만 단순한 알고리즘 또는 메타데이터 컨테이너가 아니며, 새로운 데이터에 적용하여 예측을 생성하고 관계를 추론할 수 있는 데이터, 통계 및 패턴의 집합입니다.A mining model is created by applying an algorithm to data, but it is more than an algorithm or a metadata container: it is a set of data, statistics, and patterns that can be applied to new data to generate predictions and make inferences about relationships.These metrics do not aim to answer the question of whether the data mining model answers your business question; rather, these metrics provide objective measurements that you can use to assess the reliability of your data for predictive analytics, and to guide your decision of whether to use a particular iterate on the development process.
그보다는 예측 분석을 위해 데이터의 안정성을 평가하고 개발 프로세스에서 특정 반복을 사용할지 여부를 결정하도록 돕는 데 사용할 수 있는 객관적인 측정값을 제공하는 것입니다. These metrics do not aim to answer the question of whether the data mining model answers your business question; rather, these metrics provide objective measurements that you can use to assess the reliability of your data for predictive analytics, and to guide your decision of whether to use a particular iterate on the development process.
