This last attribute is categorical, and can take the values of small, medium and big. As attributes, you have things like the name of the pastry, the prices in euros, the amount of sugar in grams and the size. Imagine you have a table with data about different types of pastry. When should you use one-hot encodingĬommonly, some attributes of the data you are working with are categorical in nature. We will take a look at the concept and then create our own implementation for encoding class attributes that we can feed into ML algorithms. Nowadays most data science toolkits let you perform one-hot encoding in very simple ways. In simple terms, it encodes categorical attributes as either 1's or 0's in vectors that represent the classes themselves. One-hot encoding is a very simple and popular way of managing categories.
Because of this, it's important to have a way to convert categorical data into a format most algorithms can work with. The main problem is that most algorithms don't really know how to manage categorical data: they are really good at working with numbers but don't really understand the concept of category. Categorical data is extremely common in most real-world machine learning applications.
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