Quick explanation: One-hot encoding

One-hot encoding is a technique used in machine learning and data preprocessing to handle categorical data. Categorical data is data that represents categories or labels, such as “red”, “blue”, “green”, or “dog”, “cat”, “bird”. These categories cannot be directly used in machine learning algorithms as they require numerical input.

One-hot encoding converts categorical data into a binary format where each category is represented by a column in a matrix with 0s and 1s. The value 1 is assigned to the corresponding category column, while all other columns are filled with 0s.

For example, if we have a dataset with a column “Color” containing values “red”, “blue”, and “green”, after one-hot encoding, the dataset will have three new columns: “Color_red”, “Color_blue”, and “Color_green”. If a row originally had the value “blue” in the “Color” column, the one-hot encoded columns would look like this: [0, 1, 0].

This encoding technique is important because it prevents the model from assuming a natural order among the categories and ensures that categorical variables are appropriately represented in the model.

Overall, one-hot encoding is a useful method for handling categorical data in machine learning and improving the performance of the models.