In a classical prediction use case, the predicted output is either a number (for regression) or category (for classification). A set of training data (x, y) where x is the input and y is the labeled output is provided to train a parameterized predictive model.
- The model is characterized by a set of parameters w
- Given an input x, for the model predicts y_hat = f(x; w) for regression, or the model predicts the probability of each possible class for classification
- Define a Lost function L(y, y_hat) for regression, or L(y, P(y=a | x), P(y=b | x) …), find the parameters w to minimize L
This problem is typically viewed as an optimization problem and uses a gradient descent approach to solve it.
