One-hot encoding is a technique used to convert categorical data into a numerical format that can be used by machine learning algorithms. It is particularly useful when dealing with categorical variables that have no ordinal relationship.
In one-hot encoding, each category is transformed into a binary vector. If there are ( n ) unique categories, each category is represented by a vector of length ( n ) where one element is “hot” (set to 1) and the rest are “cold” (set to 0). For example, if you have a categorical variable with three categories: “red,” “green,” and “blue,” one-hot encoding would represent them as:
- “red” → [1, 0, 0]
- “green” → [0, 1, 0]
- “blue” → [0, 0, 1]
One-hot encoding is used because many machine learning algorithms cannot work directly with categorical data. By converting categories into a numerical format, one-hot encoding allows these algorithms to process the data effectively. It is commonly used in preprocessing steps for machine learning models, especially in neural networks and other algorithms that require numerical input.
Implementation
cat_variables = ['Sex',
'ChestPainType',
'RestingECG',
'ExerciseAngina',
'ST_Slope'
]
# This will replace the columns with the one-hot encoded ones and keep the columns outside 'columns' argument as it is.
df = pd.get_dummies(data = df, prefix = cat_variables, columns = cat_variables)