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 ==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.
+[[Feature]] is an [[input]] variable to a [[machine learning model]]. An [[example]] consists of 1 or more features.
+[[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 also known as [[label]].
+Features are an integral component of machine learning [[algorithm]]s, as they influence the accuracy and effectiveness of the resulting models. By selecting relevant features and creating a suitable model, machine learning algorithms can learn patterns and relationships within data and use them to make predictions on new, unseen data sets.
+==Example==
-Features are an integral component of machine learning algorithms, as they influence the accuracy and effectiveness of the resulting models. By selecting relevant features and creating a suitable model, machine learning algorithms can learn patterns and relationships within data and use them to make predictions on new, unseen data sets.
 ==Types of Features==