One-hot encoding in machine learning is the conversion of categorical information into a format that may be fed into machine learning algorithms to improve prediction accuracy.
One-hot encoding is a common method for dealing with categorical data in machine learning. Categorical variables must be changed in the pre-processing section since machine learning models require numeric input variables. Nominal or ordinal data can be found in categorical data.
This approach creates a new column for each unique value in the original category column. The zeros and ones are subsequently put in these dummy variables (1 meaning TRUE, 0 meaning FALSE).
Because this procedure generates several new variables, it is prone to causing a large problem (too many predictors) if the original column has a large number of unique values.
Another disadvantage of one-hot encoding is that it produces multicollinearity among the various variables, lowering the model’s accuracy.
In addition, you may wish to transform the values back to categorical form so that they may be displayed in your application.
Value for every special category is allocated an integer number in ordinal encoding.
For instance, “purple” equals 1, “blue” equals 2, and “orange” equals 3.
It’s referred to as ordinal encoding. Integer values beginning with zero are often utilized.
This kind of encoding might be sufficient for some variables. There is an order between integer numbers, which ML algorithms might be capable to grasp and exploit.
It creates an ordinal relationship between categorical variables where none previously existed. This can trigger problems, so instead use a one-shot encoding process.
If a particular order is needed, the “categories” option can be used to provide a list containing the order of predicted labels.
The use of class can be demonstrated by transforming the color categories “purple”, “blue”, and “orange” into numbers.
A dummy variable representation is essential for some models, in addition to being significantly less redundant.
A one-hot encoding, for example, will cause the matrix of input data to become singular, meaning it cannot be inverted and the linear regression coefficients cannot be calculated using linear algebra in the case of a linear regression model.
Instead, a dummy variable encoding must be utilized for these types of models. We rarely run across this issue while analyzing machine learning algorithms in reality, unless we’re using linear regression.
The encoding can be inadequate at best for categorical variables with no ordinal relationship. Enabling the model for assumption of order among categories rather than forcing an ordinal relation through an ordinal encoding can lead to low performance or unexpected results.
Because there are three categories in this color variable, then three binary variables are required. The color is represented by a number 1 value in this binary variable, whereas the rest colors are represented by 0 values.
Since the categories are strings, they are sorted alphabetically first, and after that binary variables are generated for all categories individually. This means that blue is [1, 0, 0], the first variable being a 1, followed by orange, and finally purple.
If you don’t have a label list, the encoder will be fitted to the existing training data, which will almost certainly provide surely one example of all predicted labels for all variables. If new data already contains categories that aren’t present in the training set, the “handle unknown” option can be placed to ignore in order to avoid an error.
So to summarize, when to use one-hot encoding? In a situation where data has no relation to each other. The order of integers is treated as a significant characteristic by machine learning algorithms. In other words, a larger number will be interpreted as better or more significant than a smaller number.
While this is useful in some ordinal scenarios, certain input data lacks a ranking for category values, which can cause problems with predictions and performance. That’s why we use a one-hot encoding.
For our output values, we choose one-hot encoding in particular because it delivers more complex predictions than single labels.
Benefits of one-hot encoding → Training data is more usable and expressive as a result of one-hot encoding, and it can be rescaled easily