<bos><start_of_turn>system
You are a helpful AI assistant.
lista de codigos linguisticos disponibles: ["de", "en"].<end_of_turn>
<start_of_turn>user
Eine kurze Geschichte des statistischen Lernens<end_of_turn>
<start_of_turn>model
Though the term statistical learning is fairly new, many of the concepts
that underlie the field were developed long ago. At the beginning of the
nineteenth century, Legendre and Gauss published papers on the method
of least squares, which implemented the earliest form of what is now known
as linear regression. The approach was first successfully applied to problems
in astronomy. Linear regression is used for predicting quantitative values,
such as an individual’s salary. In order to predict qualitative values, such as
whether a patient survives or dies, or whether the stock market increases
or decreases, Fisher proposed linear discriminant analysis in 1936. In the
1940s, various authors put forth an alternative approach, logistic regression.
In the early 1970s, Nelder and Wedderburn coined the term generalized
linear models for an entire class of statistical learning methods that include
both linear and logistic regression as special cases.
By the end of the 1970s, many more techniques for learning from data
were available. However, they were almost exclusively linear methods, because fitting non-linear relationships was computationally infeasible at the
time. By the 1980s, computing technology had finally improved sufficiently
that non-linear methods were no longer computationally prohibitive. In mid
1980s Breiman, Friedman, Olshen and Stone introduced classification and
regression trees, and were among the first to demonstrate the power of a
detailed practical implementation of a method, including cross-validation
for model selection. Hastie and Tibshirani coined the term generalized additive models in 1986 for a class of non-linear extensions to generalized linear
models, and also provided a practical software implementation.
Since that time, inspired by the advent of machine learning and other
disciplines, statistical learning has emerged as a new subfield in statistics,
focused on supervised and unsupervised modeling and prediction. In recent
years, progress in statistical learning has been marked by the increasing
availability of powerful and relatively user-friendly software, such as the
popular and freely available R system. This has the potential to continue
the transformation of the field from a set of techniques used and developed
by statisticians and computer scientists to an essential toolkit for a much
broader community.<end_of_turn><eos>