Column to predict
In machine learning, the column to predict, also known as the target variable or response variable, is the column whose value is being predicted by the model. The target variable is dependent on the input features and the goal of the model is to learn the relationship between the input features and the target variable in order to make accurate predictions.
For example, in a supervised learning problem, the input features may be age, income, education, and occupation, and the target variable may be the purchase decision of a customer. The goal of the model is to learn the relationship between the input features and the target variable and make predictions on new, unseen data.
In some cases, the target variable can be binary (e.g. yes/no) or continuous (e.g. salary). The type of target variable will influence the type of model used for prediction and the evaluation metrics used to measure the performance of the model.
For example, in a supervised learning problem, the input features may be age, income, education, and occupation, and the target variable may be the purchase decision of a customer. The goal of the model is to learn the relationship between the input features and the target variable and make predictions on new, unseen data.
In some cases, the target variable can be binary (e.g. yes/no) or continuous (e.g. salary). The type of target variable will influence the type of model used for prediction and the evaluation metrics used to measure the performance of the model.
Updated on: 31/01/2023
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