From sklearn import preprocessing le preprocessingLabelEncoder lefit 1 2 2 6 LabelEncoder leclasses_ array 1 2 6 letransform 1 1 2 6 array 0 0 1 2 leinverse_transform 0 0 1 2 array.

Example of label encoding. For instance if the value of the categorical variable has six different classes we will use 0 1 2 3 4 and 5. We could choose to encode it like this. When LabelEncoder is used with categorical features having multiple values the integer value such as 0 1 2 3 etc.

Examples of the features we might need to encode. LabelEncoder can be used to normalize labels. Suppose we have a column Height in some dataset.

We apply Label Encoding on iris dataset on the target column which is Species. Understanding Label Encoding. Categorical encoding using Label-Encoding and One-Hot-Encoder.

Harvard Rutgers UCLA Berkeley Stanford. The state someone lives in eg. Now let us understand label encoding with the data of COVID-19 cases in India across states as an example.

This is actually categorical data and there is no relation of any kind between the rows. Blue 1 Green 2 Red 3 and create an object with this mapping to then use for transforming new data in a similar fashion. Beginmatrix beginarraycc textPosition textSalary hline textCustomer Service 44000 textManager 75000 textAssistant Manager 65000 textDirector 90000 endarray endmatrix.

For example we have encoded a set of country names into numerical data. In many Machine-learning or Data Science activities the data set might contain text or categorical values basically non-numerical values. For example the body_style column contains 5 different values.