What is test data and training data in data mining?
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Separating data into training and testing sets is an important part of evaluating data mining models. Typically, when you separate a data set into a training set and testing set, most of the data is used for training, and a smaller portion of the data is used for testing. Analysis Services randomly samples the data to help ensure that the testing and training sets are similar. By using similar data for training and testing, you can minimize the effects of data discrepancies and better understand the characteristics of the model.
After a model has been processed by using the training set, you test the model by making predictions against the test set. Because the data in the testing set already contains known values for the attribute that you want to predict, it is easy to determine whether the model's guesses are correct.
Creating Test and Training Sets for Data Mining Structures
In SQL Server 2017, you separate the original data set at the level of the mining structure. The information about the size of the training and testing data sets, and which row belongs to which set, is stored with the structure, and all the models that are based on that structure can use the sets for training and testing.
You can define a testing data set on a mining structure in the following ways:
Using the Data Mining Wizard to divide the mining structure when you create it.
Modifying structure properties in the Mining Structure tab of the Data Mining Designer.
Creating and modifying structures programmatically by using Analysis Management Objects (AMO) or XML Data Definition Language (DDL).
Using the Data Mining Wizard to Divide a Mining Structure
By default, after you have defined the data sources for a mining structure, the Data Mining Wizard will divide the data into two sets: one with 70 percent of the source data, for training the model, and one with 30 percent of the source data, for testing the model. This default was chosen because a 70-30 ratio is often used in data mining, but with Analysis Services you can change this ratio to suit your requirements.
You can also configure the wizard to set a maximum number of training cases, or you can combine the limits to allow a maximum percentage of cases up to a specified maximum number of cases. When you specify both a maximum percentage of cases and a maximum number of cases, Analysis Services uses the smaller of the two limits as the size of the test set. For example, if you specify 30 percent holdout for the testing cases, and the maximum number of test cases as 1000, the size of the test set will never exceed 1000 cases. This can be useful if you want to ensure that the size of your test set stays consistent even if more training data is added to the model.
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After a model has been processed by using the training set, you test the model by making predictions against the test set. Because the data in the testing set already contains known values for the attribute that you want to predict, it is easy to determine whether the model's guesses are correct.
Creating Test and Training Sets for Data Mining Structures
In SQL Server 2017, you separate the original data set at the level of the mining structure. The information about the size of the training and testing data sets, and which row belongs to which set, is stored with the structure, and all the models that are based on that structure can use the sets for training and testing.
You can define a testing data set on a mining structure in the following ways:
Using the Data Mining Wizard to divide the mining structure when you create it.
Modifying structure properties in the Mining Structure tab of the Data Mining Designer.
Creating and modifying structures programmatically by using Analysis Management Objects (AMO) or XML Data Definition Language (DDL).
Using the Data Mining Wizard to Divide a Mining Structure
By default, after you have defined the data sources for a mining structure, the Data Mining Wizard will divide the data into two sets: one with 70 percent of the source data, for training the model, and one with 30 percent of the source data, for testing the model. This default was chosen because a 70-30 ratio is often used in data mining, but with Analysis Services you can change this ratio to suit your requirements.
You can also configure the wizard to set a maximum number of training cases, or you can combine the limits to allow a maximum percentage of cases up to a specified maximum number of cases. When you specify both a maximum percentage of cases and a maximum number of cases, Analysis Services uses the smaller of the two limits as the size of the test set. For example, if you specify 30 percent holdout for the testing cases, and the maximum number of test cases as 1000, the size of the test set will never exceed 1000 cases. This can be useful if you want to ensure that the size of your test set stays consistent even if more training data is added to the model.
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Separating data into training and testing sets is an important part of evaluating data mining models. Typically, when you separate a data set into a training set and testing set, most of the data is used for training, and a smaller portion of the data is used for testing.
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