Label encoding refers to converting labels to a numeric form in order to convert it to a machine-readable form. Machine learning algorithms can then better figure out how these labels should work. This is an important preprocessing step for a structured dataset in supervised learning.
Suppose we have a column in some dataset.
After applying the label encoding, the Height column is converted to:
where 0 — label for tall, 1 — label for middle and 2 — for short stature.
We are applying the tag encoding to the
iris dataset in the destination column, which is the View. Contains three species Iris-setosa, Iris-versicolor, Iris-virginica .
array ([`Iris-setosa`,` Iris-versicolor`, `Iris-virginica`], dtype = object) pre>
After applying Label Encoding —
array ([0, 1, 2], dtype = int64) pre>
Label constraint Encoding
An encoding label converts data into machine-readable form, but assigns a unique number (starting at 0) to each data class. This can lead to the formation of a priority problem when training datasets. A high value label is considered to have higher priority than a lower value label.
Attribute having output classes Mexico , Paris , Dubai . On the Coding label of this column, let mexico be replaced with 0 , Paris replaced with 1, and Dubai b> is replaced by 2.
It can be interpreted that Dubai has a higher priority when training the model than Mexico and Paris b >, but in fact there is no such priority relationship between these cities.