<bos><start_of_turn>system
You are a helpful AI assistant.
lista de codigos linguisticos disponibles: ["en", "en"].<end_of_turn>
<start_of_turn>user
Regression Versus Problems<end_of_turn>
<start_of_turn>model
Variables can be characterized as either quantitative or qualitative (also quantitative
qualitative known as categorical). Quantitative variables take on numerical values.
Examples include a person’s age, height, or income, the value of a house, categorical
and the price of a stock. In contrast, qualitative variables take on values in one of K different classes, or categories. Examples of qualitative class variables include a person’s gender (male or female), the brand of product purchased (brand A, B, or C), whether a person defaults on a debt
(yes or no), or a cancer diagnosis (Acute Myelogenous Leukemia, Acute
Lymphoblastic Leukemia, or No Leukemia). We tend to refer to problems
with a quantitative response as regression problems, while those involv- regression
ing a qualitative response are often referred to as classification problems. classification
However, the distinction is not always that crisp. Least squares linear regression (Chapter 3) is used with a quantitative response, whereas logistic
regression (Chapter 4) is typically used with a qualitative (two-class, or
binary) response. As such it is often used as a classification method. But binary
since it estimates class probabilities, it can be thought of as a regression
2.2 Assessing Model Accuracy 29
method as well. Some statistical methods, such as K-nearest neighbors
(Chapters 2 and 4) and boosting (Chapter 8), can be used in the case of
either quantitative or qualitative responses.
We tend to select statistical learning methods on the basis of whether
the response is quantitative or qualitative; i.e. we might use linear regression when quantitative and logistic regression when qualitative. However,
whether the predictors are qualitative or quantitative is generally considered less important. Most of the statistical learning methods discussed in
this book can be applied regardless of the predictor variable <end_of_turn><eos>