<bos><start_of_turn>system
You are a helpful AI assistant.
lista de codigos linguisticos disponibles: ["fr", "en"].<end_of_turn>
<start_of_turn>user
Méthodes paramétriques<end_of_turn>
<start_of_turn>model
Parametric methods involve a two-step model-based approach.

1. First, we make an assumption about the functional form, or shape, of f. For example, one very simple assumption is that f is linear in X: f(X) = β0 + β1X1 + β2X2 + ... + βpXp.
This is a linear model. Once we have assumed that f is linear, the problem of estimating f is greatly simplified. Instead of having to estimate an entirely arbitrary p-dimensional function f(X), one only needs to estimate the p + 1 coefficients β0, β1,...,βp.

2. After a model has been selected, we need a procedure that uses the training data to fit or train the model. In the case of the linear model fit train, we need to estimate the parameters β0, β1,...,βp. That is, we want to find values of these parameters such that Y ≈ β0 + β1X1 + β2X2 + ... + βpXp. The most common approach to fitting the model is referred to as (ordinary) least squares. <end_of_turn><eos>