Principal Component Analysis

Machine Learning (ML) modeling involves finding patterns in the data under consideration. In supervised learning, the model learns patterns through labeled data; that is, the data provided has the independent variables and the dependent variable. Based on the field, independent variables may have other names like explanatory, predictor, regressor, covariate, feature (in machine learning and pattern recognition), input, or control. On the other hand, the dependent variable is also called target, label, response, predicted, output, regressand, outcome, explained, or measured.