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One Hot Encoding
Converts one or more input text columns into as many columns of one-hot encoded vectors. One-hot encoding is often used to convert categorical data into a form that can be provided to a machine learning algorithm.
Inputs
| Name | ID | Description | Type |
|---|---|---|---|
| Data | Data | The data set to change. | Generic |
| Columns | Columns | The names of the columns in the data set to encode. | Text |
| Output Kind | Output Kind | Output Kind: \n (1) Bag - produces one vector such that each slot stores the number of occurances of the corresponding value in the input vector. Each slot in this vector corresponds to a value in the dictionary, so its length is the size of the built dictionary. Indicator and Bag differ simply in how the bit-vectors generated from individual slots in the input column are aggregated: for Indicator they are concatenated and for Bag they are added. When the source column is a Scalar, the Indicator and Bag options are identical.\n (2) Indicator - produces an indicator vector. Each slot in this vector corresponds to a category in the dictionary, so its length is the size of the built dictionary. If a value is not found in the dictioray, the output is the zero vector.\n (3) Key - produces keys in a KeyDataViewType column. If the input column is a vector, the output contains a vectory key type, where each slot of the vector corresponds to the respective slot of the input vector. If a category is not found in the bulit dictionary, it is assigned the value zero.\n (4) Binary - produces a binary encoded vector to represent the values found in the dictionary that are present in the input column. If a value in the input column is not found in the dictionary, the output is the zero vector.\n | Any |
Outputs
| Name | ID | Description | Type |
|---|---|---|---|
| Data | Data | The modified data set. | Generic |
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