![]() Attributes which we want to predict need to be assigned the role of label attributes (sometimes also called target or class attributes) – these are used as outputs by machine learning operators. All of our attributes currently have a regular role. Attributes without an assigned role are by default regular attributes – these are used as inputs by machine learning operators. Role describes how the attribute will be used by machine learning operators. Type defines the attribute’s (column’s) possible values, e.g. Note the metadata table, and two of its columns, Role and Type. ![]() Click on the created Retrieve Altoona Crime Rates operator, and hover the mouse over its output port: wait for a small window to pop up, and display some metadata about the dataset.Drag the stored Altoona Crime Rates ( not the combined dataset) into the Process.In our modeling, we use a classic machine learning method called Decision Tree. We then predict the profile of Altoona offenders most likely to commit a crime based on sex, age, race, and ethnicity. determining which column (or variable) should be predicted by what other columns (or variables) in our dataset. In this tutorial, we prepare data for modeling by changing column types and roles, i.e. Retrieve the data, and note its different types and roles. R Module 6: Normalization & Detecting Outliers.R Module 4: Creating & Removing Columns.RM Module 7: Pivoting & Advanced Renaming.RM Module 6: Normalization & Detecting Outliers. ![]()
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