One-hot encoding

A vibrant dance of transformation, one-hot encoding converts categorical variables into binary vectors, like a kaleidoscope of colors turning into a pattern of black and white, allowing machine learning models to gracefully interpret and analyze the qualitative attributes within a dataset.

Example

Imagine a dataset containing information about different types of fruit, with a categorical variable representing the fruit's color (e.g., 'red', 'green', or 'yellow'). Machine learning models typically require numerical input, so one-hot encoding can be used to transform the color variable into binary vectors. For each unique category, a new binary column is created, and a '1' is placed in the corresponding column for the relevant category, while '0's are placed in the other columns. In this case, 'red' might become [1, 0, 0], 'green' would be [0, 1, 0], and 'yellow' would be [0, 0, 1]. This enables the model to process and learn from the categorical information.