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into categorical fields. \nThere are two available splitting options: splitting by values and binning.\nThe Sample Snap is a Flow type Snap that enables you to generate a sample dataset from the input dataset.\nThis sampling is carried out based on one of the four available algorithms and with a predefined pass through percentage.\nThe Scale Snap is a Transform type Snap that scales numeric values in fields to specific ranges or applies statistical transformations.\nThe Snap helps you with data preparation before applying a machine learning algorithm to the data. \nScale Snap supports four kinds of transformation.\nThe Shuffle Snap is a a Flow type Snap that enables you to randomize the order of the rows in an incoming dataset.\nThe Snap can be optimized to work with large datasets by configuring the maximum percentage of memory that can be used to buffer the dataset if that limit is exceeded then the dataset is downloaded to a temporary file in local storage. \nA random integer can be assigned as the seed value a seed value is any number that acts as the identifier for a particular randomized order. \nThe Snap produces the same randomized order for the same seed value. \nThis Snap along with the Sample Snap is helpful in randomizing a sample dataset for further analysis. \nThis is a Transform type Snap that is used to detect and convert the data type of the incoming documents.\nIt has the ability to automatically derive the type of each value. \nYou can also specify your preference for handling certain fields in the input document. \nThis is a Transform type Snap that performs K-fold Cross Validation on a classification & regression dataset.\nCross validation is a technique for evaluating ML algorithms by splitting the original dataset into K equally-sized chunks.\nThe average accuracy and other statistics are computed to be used to select the most suitable algorithm for the dataset.\nThis is a Transform type Snap that performs K-fold Cross Validation on a  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