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The OneHotEncoder() performs one hot encoding. One hot encoding consists in replacing the categorical variable by a group of binary variables which take value 0 or 1, to indicate if a certain category is present in the observation. The binary variables are also known as dummy variables.

Machine learning methods such as logistic regression, SVM with a linear kernel, and so on, will often require that categorical variables be converted into dummy variables. For example, a single feature Vehicle would be converted into three features, Cars, Trucks, and Pickups, one for each category in the categorical feature. The common ways to preprocess categorical features are:
        i) pandas,
        ii) scikit-learn.

One-hot-encoding is a process by which categorical variables are converted into a form that could be provided to neural networks to do a better job in prediction.

tf.feature_column.categorical_column_with_identity offers the best way to encode categorical data that is already indexed, i.e. has integers in [0-N].

Note that decision trees can be used for both classification and regression tasks, and they are capable of handling both categorical and numerical variables.

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Preprocessing categorical features. code:          
         
Output: