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There are two available encoding options: Integer Encoding and One Hot Encoding.\nThe Clean Missing Values is a Transform type Snap that is used to handle missing values in an incoming dataset by dropping or imputing values. The Clean Missing Values Snap supports four approaches.\nThe Date Time Extractor is a Transform type Snap that is used to extract components from datetime data and add them to the result field. You can use the Snap to prepare the data before performing aggregation or analysis. For example to identify the total sales for each month or quarter. \nThis is a Transform type Snap that converts numeric fields into categorical fields. There 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. This 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. The Snap helps you with data preparation before applying a machine learning algorithm to the data. Scale 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. The 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. A 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. The Snap produces the same randomized order for the same seed value. This 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. It has the ability to automatically derive the type of each value. You 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 dataset. Cross validation is a technique for evaluating ML algorithms by splitting the original dataset into K equally-sized chunks. K is the number of folds. In each of the K iterations K-1 chunks are used to train the model while the last chunk is used as a test set. The 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 regression dataset. Cross validation is a technique for evaluating ML algorithms by splitting the original dataset into K equally-sized chunks. K is the number of folds. In each of the K iterations K-1 chunks are used to train the model while the last chunk is used as a test set. 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