Data Preprocessing Questions Medium
One-hot encoding is a technique used in data preprocessing to convert categorical variables into a binary vector representation. It is used when dealing with categorical data that cannot be directly used in mathematical models or algorithms.
In one-hot encoding, each category is represented by a binary vector where all elements are zero except for the element corresponding to the category, which is set to one. This allows the categorical variable to be represented as a numerical feature that can be used in various machine learning algorithms.
One-hot encoding is used when the categorical variable does not have an inherent order or hierarchy. It is commonly used in tasks such as classification, where the presence or absence of a category is important, but the magnitude or order of the categories is not relevant.
For example, consider a dataset with a categorical variable "color" that can take values like "red," "blue," and "green." By applying one-hot encoding, this variable can be transformed into three binary features: "color_red," "color_blue," and "color_green." Each feature will have a value of 1 if the corresponding category is present and 0 otherwise.
Overall, one-hot encoding is a useful technique in data preprocessing to convert categorical variables into a format that can be effectively used in machine learning algorithms.