In machine learning, we usually deal with datasets which contains multiple labels in one or more than one columns. These labels can be in the form of words or numbers. To make the data understandable or in human readable form, the training data is often labeled in words.
Label Encoding refers to converting the labels into numeric form so as to convert it into the machine-readable form. Machine learning algorithms can then decide in a better way on how those labels must be operated. It is an important pre-processing step for the structured dataset in supervised learning.
Suppose we have a column Height in some dataset.
After applying label encoding, the Height column is converted into:
where 0 is the label for tall, 1 is the label for medium and 2 is label for short height.
We apply Label Encoding on
iris dataset on the target column which is Species. It contains three species Iris-setosa, Iris-versicolor, Iris-virginica.
array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)
After applying Label Encoding –
array([0, 1, 2], dtype=int64)
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