Why is hot encoding necessary?
A one hot encoding allows the representation of categorical data to be more expressive. Many machine learning algorithms cannot work with categorical data directly. This is required for both input and output variables that are categorical. We could use an integer encoding directly, rescaled where needed.
Why is hot encoding bad?
2. One-Hot Encoding. For categorical variables where no such ordinal relationship exists, the integer encoding is not enough. In fact, using this encoding and allowing the model to assume a natural ordering between categories may result in poor performance or unexpected results (predictions halfway between categories).
When do you Drop one-hot encoding on a categorical variable?
Recently someone pointed out that when you do one-hot encoding on a categorical variable you end up with correlated features, so you should drop one of them as a “reference”.
Which is the safest way to hot encoding and drop?
This question, in a slightly different form, was discussed herein earlier. You are kind of right, but the best and safest way is to do One-Hot-Encoding and drop at the end because which column you want to drop at the very beginning?
Do you always drop the first column after performing one hot encoding?
Since one of the columns can be generated completely from the others, and hence retaining this extra column does not add any new information for the modelling process, would it be good practice to always drop the first column after performing One Hot encoding, regardless of the algorithm of choice ?
How does one hot encoding and the dummy variable trap work?
The dummy variable trap manifests itself directly from one-hot-encoding applied on categorical variables. As discussed earlier, size of one-hot vectors is equal to the number of unique values that a categorical column takes up and each such vector contains exactly one ‘1’ in it. This ingests multicollinearity into our dataset.