| To apply the label encoder first you need to upload the dataset (.csv format only). To upload the file click on the Choose File and the Open window will pop-up. After selecting the dataset (.csv format only) and by clicking open all features (variables) in the dataset containing the string values will be transformed in the number format. After process is complete you can click on Download the file. |
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A label encoder is a tool which is commonly used in artificial intelligence (machine learning) to transform the categorical data into numerical format.
For every feature/variable (column in .csv dataset) containing the categorical values (string format) the label encoder transforms these string format to numerical values. This step is often a crucial preprocessing step since majority of machine learning algorithms require all input features in numeric format.
The process of label encoder consists of the several steps i.e.:
The advantages of label encoder can be described with two words i.e. simplicity and compatibility.
The Label encoder is straightforward and easy to implement. The label encoder is essential due to the fact that many machine learning algorithms require numerical input so the transformation of the features in string format to numerical format in the dataset is necessary.
The label encoding can introduce a sense of ordina relationship between categories that might not actually exist. For example, encoding "Cat", "Dog", "Rabbit" as 0, 1, 2 might misleadingly imply a raking.
The label encoded data works really well with tree-based algorithms such as decision trees, random forests, and gradient boosting algorithms since they are not sensitive to the magnitude of numbers. The label encoding is appropriate if the categorical data has an intrinsic order i.e. "Low", "Medium", and "High".
The alternatives to the label encoder are one-hot encoding and ordinal encoding. The One-Hot encoding converts each category into a binary vector which is suitable when there is no ordinal relationship between categories. The good example of one-hot encoding would be a "color" feature where color would be encoded into separate binary columns for each color.
For every feature/variable (column in .csv dataset) containing the categorical values (string format) the label encoder transforms these string format to numerical values. This step is often a crucial preprocessing step since majority of machine learning algorithms require all input features in numeric format.
How does label encoder works ?
The process of label encoder consists of the several steps i.e.:
- Mapping the categories to numbers - The label encoder assigns unique integer values to each category in a categorical feature. For example, if you have features called Red, Green, and Blue the label encoder might transform this into 0, 1, and 2.
- Transformation - during the transformation process the encoder converts the categorical values to their corresponding numeric codes based on the mapping.
Advantages of Label Encoder
The advantages of label encoder can be described with two words i.e. simplicity and compatibility.
The Label encoder is straightforward and easy to implement. The label encoder is essential due to the fact that many machine learning algorithms require numerical input so the transformation of the features in string format to numerical format in the dataset is necessary.
Disadvantages of Label Encoder
The label encoding can introduce a sense of ordina relationship between categories that might not actually exist. For example, encoding "Cat", "Dog", "Rabbit" as 0, 1, 2 might misleadingly imply a raking.Use cases and alternatives
The label encoded data works really well with tree-based algorithms such as decision trees, random forests, and gradient boosting algorithms since they are not sensitive to the magnitude of numbers. The label encoding is appropriate if the categorical data has an intrinsic order i.e. "Low", "Medium", and "High".The alternatives to the label encoder are one-hot encoding and ordinal encoding. The One-Hot encoding converts each category into a binary vector which is suitable when there is no ordinal relationship between categories. The good example of one-hot encoding would be a "color" feature where color would be encoded into separate binary columns for each color.
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