i need clarity with prediction of X test, X train, y test, y train
So our goal in training is given some features or dependant variables or X ind a model that predicts the independent variable or y from X. We mostly do that by minimizing some loss function f(M(X), y). When it comes to testing, we want to see how good our model is when applied to examples that were not part of the training set. So we take our trained model M, feed it with our features X_test and check how good it predicts y_test.
in a linear regression model let's say we have 3 independent variable (age, height, gender) and one dependent variable (diabetic) and then we split the model as, X train- i.e. (say 70%) data of independent variables for training, X test-> i.e. 30% data of independent variables for testing
y train-> i.e. (say 70%) data of dependent variable for training, y test-> i.e. 30% data of dependent variables for testing
so when we do predict X-test, or predict X-test, are we predicting values of independent variables or are we predicting the dependent variable (diabetic?)