Onehotkoodausta

Onehotkoodaus, also known as one-hot encoding, is a process used in machine learning and data preprocessing to represent categorical variables as numerical vectors. In this method, each category within a variable is assigned a unique binary vector. The length of this vector is equal to the total number of unique categories. For a given data point, the vector will have a '1' at the position corresponding to its category and '0's elsewhere. For example, if a variable "color" has categories "red", "blue", and "green", "red" might be represented as [1, 0, 0], "blue" as [0, 1, 0], and "green" as [0, 0, 1].

This technique is crucial because many machine learning algorithms, particularly those based on mathematical operations like gradient descent, require numerical input. Simply assigning arbitrary integers to categories (e.g., red=1, blue=2, green=3) can introduce unintended ordinal relationships, implying that "blue" is somehow "greater" than "red," which is usually not the case for nominal categorical data. Onehotkoodaus avoids this by creating independent dimensions for each category, preventing such misleading interpretations. It ensures that each category is treated distinctly, preserving the nominal nature of the data. While effective, it can lead to high dimensionality if a categorical variable has many unique values, potentially increasing computational cost and the risk of overfitting.