What are predictors in machine learning?

What are predictors in machine learning?

• A. Variables that measure model performance
• B. Variables used to predict the value of the target variable
• C. Constants that determine algorithm stability
• D. Data points used for model training

Answer: (B)

Predictors in machine learning refer specifically to the variables that are used to estimate or predict the value of a target variable. These predictors, also known as features or independent variables, are essential in training a model to make forecasts based on input data. In the context of supervised learning, the model learns the relationship between these predictors and the target variable during the training phase. When a model is deployed, it utilizes the values of these predictors to make predictions about the target variable for new, unseen data. Understanding the role of predictors is fundamental to building accurate models, as the choice and quality of these variables can significantly influence the outcomes and efficacy of the predictive analytics process. The other options do not accurately capture the definition of predictors in machine learning, focusing instead on aspects such as model performance, constants, or data points used for training, which are not the defining characteristic of predictors.

Predictors in machine learning refer specifically to the variables that are used to estimate or predict the value of a target variable. These predictors, also known as features or independent variables, are essential in training a model to make forecasts based on input data.

In the context of supervised learning, the model learns the relationship between these predictors and the target variable during the training phase. When a model is deployed, it utilizes the values of these predictors to make predictions about the target variable for new, unseen data. Understanding the role of predictors is fundamental to building accurate models, as the choice and quality of these variables can significantly influence the outcomes and efficacy of the predictive analytics process.

The other options do not accurately capture the definition of predictors in machine learning, focusing instead on aspects such as model performance, constants, or data points used for training, which are not the defining characteristic of predictors.