Python Machine Learning By Example

One-hot-encoding

The one-of-K or one-hot-encoding scheme uses dummy variables to encode categorical features. Originally it was applied to digital circuits. The dummy variables have binary values like bits, so they take the values zero or one (equivalent to true or false). For instance, if we want to encode continents, we will have dummy variables, such as is_asia, which will be true if the continent is Asia and false otherwise. In general, we need as many dummy variables, as there are unique labels minus one. We can determine one of the labels automatically from the dummy variables, because the dummy variables are exclusive. If the dummy variables all have a false value, then the correct label is the label for which we don't have a dummy ...