11/8/2022 0 Comments Ibm spss trainingRight-click on the Analysis node and click Run.įrom the Outputs tab on the right, click on the “eye” icon next to analysis of to gain insight into the accuracy of the results.Įxpand the Graphs tab, then drag and drop the Evaluation node onto the canvas. Connect the Risk golden nugget node to the Analysis node. Right-click on the new Risk golden nugget node and choose Preview to inspect the output results.Įxpand the Outputs tab, then drag and drop an analysis node onto the canvas. When the execution is done, you will see a new golden nugget-like Risk node added to the canvas. Right-click on the Random Forest node and click Run. The Random Forest node will automatically be renamed Risk. Connect the Type node to the Random Forest node. Change the role of Risk from Input to Target, then click Save to close the tab.Įxpand the Modeling tab, then drag and drop the Random Forest node onto the canvas. Once the read operation completes, check that the measure and role for each field is correct. Connect the Data Asset node with the Type node, then double-click on the Type node to make the necessary configurations.Ĭlick on Read Values. Click on the “eye” icon to open the Data Audit (Data Audit of ) to view statistics about the data.Ĭlick X in the upper right corner to close the window.Įxpand the Field Operations tab and drag and drop the Type node onto the canvas. Once it is ready, the output can be viewed by opening the Outputs menu on the right. Alternatively, right-click on the Data Audit node and click Run. Hover over the Data Audit node and click on the three vertical dots to open the menu for the node. Click on the icon and drag over to the Data Audit node. Hover over the Data Asset node that was dragged and dropped on the canvas earlier, and it should show a blue circular icon on the side. To gain insight into your data, open the Output tab and drag and drop the data audit node onto the canvas. On the Assets page, open the Data Assets tab, choose the german_credit_data.csv file you previously uploaded and click Select. Double-click on the node that was dropped on the canvas and click Change data asset. In the left-hand pane, expand Import, then drag and drop a Data Asset node on the canvas. Give the flow a meaningful name, such as Credit Risk Flow, then click Create. Upload the dataset to the analytics project by clicking on Browse and selecting the downloaded file.įrom the Project home page, click Add to Project + and choose Modeler flow. Provide a name and optional description for the project and click Create.ĭownload the dataset for this experiment and load it into you project.ĭownload the german_credit_data.csv dataset. Select the Analytics project radio button and click the Next button. Go the (☰) navigation menu and under the Projects section click on All Projects. Your project resources can include data, collaborators, and analytic assets like notebooks and models, etc. In Cloud Pak for Data, we use the concept of a project to collect / organize the resources used to achieve a particular goal (resources to build a solution to a problem). Otherwise, you can skip to Create an SPSS Modeler Flow. If you have not already created a project for this learning path, follow the instructions below to create one.
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