In machine learning, what are "predictors"?

In the context of machine learning, "predictors" refer to the variables or features used to make predictions about outcomes. These predictors are the input data that drive the modeling process, allowing the algorithm to learn patterns from the training data. By analyzing the relationships between the predictors and the target variable (the outcome we are trying to predict), the model can generate forecasts or classifications for new, unseen data.

The understanding of predictors is fundamental in constructing a machine learning model because the quality and selection of predictors directly impact the model's effectiveness and accuracy. Well-chosen predictors can significantly enhance a model's performance, while poor choices might lead to ineffective predictions. This highlights the importance of feature selection and engineering in the machine learning workflow.

In contrast, constants used in algorithm calculations do not vary based on input data and serve a different purpose, while metrics for model evaluation assess how well a model performs rather than directly influencing the predictions it makes. Data points that need classification refer to cases the model is applied to for making predictions, rather than the variables themselves used to inform those predictions.