We could perform one-hot encoding manually using tools provided by numpy and pandas. The value of these dummy variables for each possible level of Z is shown in the table below. A one-hot encoding for Z will create four new variables: Za, Zb, Zc, and Zd. A particular dummy variable is equal to one for observations with the associated level, and is zero otherwise.įor example, assume that we have a categorical variable Z with four levels: a, b, c, and d. The values found within a dummy variable will be either 0 or 1. The encoding will have one dummy variable for each level found within the categorical variable. To perform one-hot encoding on a categorical variable, we must introduce new variables, referred to as dummy variables. The most common technique for numerically encoding qualitative information is to use one-hot encoding. If we wish to use a categorical variable as a feature in a Scikit-Learn model, we can do so, but we must first find a way to numerically encode the variable. The Scikit-Learn implementations of supervised learning models require all of the feature information to be provided in a numerical format.
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