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The K-Means Clustering block enables you to apply a K-Means clustering model to a dataset. The following demonstrates how the K-Means Clustering block is used to split the input dataset lib_books.csv (containing observations that describe a range of books available from a lending library) into a specified number of…
The Join block enables you to combine observations from two datasets into a single working dataset. The following demonstrates how to use the Join block to link information in two input datasets * lib_books.csv, which contains observations that describe a range of books available from a lending library. * ddn_subjects.csv,…
The Impute block enables you to replace missing values in a dataset variable based on other values for that variable. The block is used to replace the missing values in the Price variable in an input dataset lib_books.csv (which contains observations that describe a range of books available from a lending library) based on…
The Hierarchical Clustering block enables you to apply a hierarchical clustering model to a dataset. The following demonstrates how to use the Hierarchical Clustering to split the input basketball_players.csv dataset (containing observations that describe baskteball players in a national league) and assign observations to…
The Filter block enables you to reduce the total number of observations in a large dataset. The following demonstrates how to use the Filter block to reduce the size of an input dataset loan_data.csv (containing observations each of which describes a completed loan and the person who took the loan out) using the numerical…
The Decision Tree block enables you to apply a Decision Tree predictive model to an input dataset. The following demonstrates how to use the Decision Tree block to predict a dependent Score variable from an input dataset basketball_shots.csv (containing observations that detail a basketball shot in a professional game and…
The Decision Forest block enables you to apply a decision forest predictive model to an input dataset. The following demonstrates how to use the Decision Forest block to model the Default variable from the loan_data.csv dataset (containing observations each of which describes a completed loan and the person who took the…
The Aggregate block enables you to apply a function to create a single value from a set of variable values grouped together using other variables in the input dataset. The block is used to generate basic statistics for the input loan_data.csv dataset (containing observations each of which describes a completed loan and the…
The WoE Transform block enables you to measure the influence of an independent variable on a specified dependent variable, for example to measure risk, and apply variable transformation, for example to transform variables into a finite number of bins and output the results. The following demonstrates how to use the WoE…
The Text Transform block enables you to define operations used to manipulate the contents of variables from an input dataset. The following demonstrates how to use the Text Transform block to remove punctuation from a variable in the input dataset lib_books.csv (which contains observations that describe a range of books…
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