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(Created page with "===Introduction== Machine learning takes into account features, which are quantifiable aspects or characteristics of a data point that are used to build predictive models. These elements, also referred to as predictors or independent variables, are selected based on their capacity for explaining variations in the dependent variable - that is, the target variable that the model seeks to predict. Features are an integral component of machine learning algorithms, as they i...") |
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Features can be broadly divided into three types: numerical, categorical and text. Each requires a distinct approach when preprocessing and feature engineering. | Features can be broadly divided into three types: numerical, categorical and text. Each requires a distinct approach when preprocessing and feature engineering. | ||
===Numerical Features== | ===Numerical Features=== | ||
Numeric features are variables that take on numerical values, such as age, height, weight or temperature. These features may either be continuous or discrete; continuous ones take any value within a range while discrete ones only accept specific ones. | Numeric features are variables that take on numerical values, such as age, height, weight or temperature. These features may either be continuous or discrete; continuous ones take any value within a range while discrete ones only accept specific ones. | ||