When an attribute is missing, that attribute’s part will be ignored from the entire equation. The following screenshot shows the prediction value from the Linear Regression model when the number of cars is unknown. It is important to note that if you do not have some attributes, you can still obtain the results. The following screenshot shows the query and its result. The same results can be obtained from SQL Server Management Studio by executing the DMX query. In the following example, some values are provided for a given instance to predict the annual income.įrom the result tab, results can be views as shown in the below screenshot. This can be done from the Mining Model Prediction tab, as shown in the below screenshot. Let us see how we can predict using the built Microsoft Linear Regression model. Let us see how we can predict from the built model using prediction feature.Īn important aspect of any data mining technique is to predict using the built model. This is the equation and you simply have to replace relevant values to predict the yearly income. The following screenshot shows the linear regression equation. However, as indicated before, it is a one-node tree view.įrom this view, you can get the linear regression equation, which is the final goal of this technique. In Microsoft Linear Regression, only another available view is Tree View. By sliding the slider down on the left-hand side, you can find out the significance of these attributes, as we observed in the Naïve base and Decision Trees. The Dependency network shows what the most dependent attributes to predict Yearly Income is. You can ignore this warning as for linear regression there won’t be any split for the decision trees.Īfter processing the data mining structure, we are now ready to view the results.Īs we observed in many SQL Server algorithms, in linear regression, we can find the dependency network, as shown in the below screenshot. There will be a warning message saying that there is no split in decision trees. The next is to process the data mining structure. This is the Solution Explorer for the Microsoft Linear Regression data mining technique. In the other screens in the data mining wizard, default settings are used. Content types can be modified from the following screenshot.īy default, House Owner Flag is selected Text data type, which has to be changed to the Long data type.
Though there are default Content types, there are instances where you need to change the content types. However, in the Microsoft Linear Regression, we are to predict YearlyIncome. In the previous examples, we have selected Bike Buyer as the predicted column. This is a major limitation in the Microsoft Linear Regression, which is not in the standard Linear Regression techniques. Therefore, in the above selection, Age, BikeBuyer, HouseOwnerFlag, NumberCarsOwned, NumberChildrenatHome, TotalChildren are selected as input attributes. In Microsoft Linear regression, all the inputs should be numerical the text column should not be selected.
The Customer Key is chosen as the Key from the algorithm from the above screen. The vTargetMail will be the Case table and let us choose relevant attributes, as shown in the below screenshot. Unlike in the decision trees, linear regression will have only one node, and we will verify the results for linear regression with the decision trees at the end of the article. In this technique, the Microsoft decision trees algorithm is used. We choose the Microsoft Linear Regression as the data mining technique, as shown in the below screenshot. The Data source is chosen as AdventureWorksDW and vTargetMail view is selected as the data source views. As in the previous examples, today also, we will be using the vTargetMail view in the AdventureWorksDW sample database.Īs we did for other data mining techniques, first, we need to create a data source and the Data Source View. Let us see how we can use linear regression in the Microsoft SQL Server platform. This means that the linear regression model can be represented as follows: For example, if you want to predict the house prices, you need to know the number of rooms, the area of the house, and other features of the house. In this type of technique, there are multiple independent variables from which the dependent variable is predicted. Microsoft Linear Regression is a forecasting technique. Naïve Bayes, Decision Trees, Time Series, Association Rules, and Clustering are the other techniques that we discussed until today.
This is the next data mining topic in our SQL Server Data mining techniques series. In this article, we will be discussing Microsoft Linear Regression in SQL Server.