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LogoutOne hot encoder and dummy encoder are two powerful and effective encoding schemes. They are also very popular among the data scientists, But may not be as effective when-
A large number of levels are present in data. If there are multiple categories in a feature variable in such a case we need a similar number of dummy variables to encode the data. For example, a column with 30 different values will require 30 new variables for coding.
If we have multiple categorical features in the dataset similar situation will occur and again we will end to have several binary features each representing the categorical feature and their multiple categories e.g a dataset having 10 or more categorical columns.
In both the above cases, these two encoding schemes introduce sparsity in the dataset i.e several columns having 0s and a few of them having 1s. In other words, it creates multiple dummy features in the dataset without adding much information.
Also, they might lead to a Dummy variable trap. It is a phenomenon where features are highly correlated. That means using the other variables, we can easily predict the value of a variable.
Due to the massive increase in the dataset, coding slows down the learning of the model along with deteriorating the overall performance that ultimately makes the model computationally expensive. Further, while using tree-based models these encodings are not an optimum choice.