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class OneHotEncoder

A way to deal with categorical features that is particularly useful for unordered (i.e. nominal) data.

It replaces a column with a set of columns, each representing a unique value in the original column. The value of each new column is 1 if the original column had that value, and 0 otherwise. Take the following table as an example:

col1
"a"
"b"
"c"
"a"

The one-hot encoding of this table is:

col1__a col1__b col1__c
1 0 0
0 1 0
0 0 1
1 0 0

The name "one-hot" comes from the fact that each row has exactly one 1 in it, and the rest of the values are 0s. One-hot encoding is closely related to dummy variable / indicator variables, which are used in statistics.

Parent type: InvertibleTableTransformer

Source code in one_hot_encoder.sdsstub 
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class OneHotEncoder() sub InvertibleTableTransformer {
    /**
     * Learn a transformation for a set of columns in a table.
     *
     * This transformer is not modified.
     *
     * @param table The table used to fit the transformer.
     * @param columnNames The list of columns from the table used to fit the transformer. If `None`, all columns are used.
     *
     * @result result1 The fitted transformer.
     */
    @Pure
    fun fit(
        table: Table,
        @PythonName("column_names") columnNames: List<String>?
    ) -> result1: OneHotEncoder
}

fun fit

Learn a transformation for a set of columns in a table.

This transformer is not modified.

Parameters:

Name Type Description Default
table Table The table used to fit the transformer. -
columnNames List<String>? The list of columns from the table used to fit the transformer. If None, all columns are used. -

Results:

Name Type Description
result1 OneHotEncoder The fitted transformer.
Source code in one_hot_encoder.sdsstub 
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@Pure
fun fit(
    table: Table,
    @PythonName("column_names") columnNames: List<String>?
) -> result1: OneHotEncoder