<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. (2.4)
This is a linear model, which will be discussed extensively in Chapter 3. 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 (2.4), 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 (2.4) is referred to
as (ordinary) least squares, which we discuss in Chapter 3. However, least squares
least squares is one of many possible ways to fit the linear model. I<end_of_turn><eos>