Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách gia công, Bìa mềm
An Introduction to Statistical Learning, With Applications in R (ISLR)
— first published in 2013, with a second edition in 2021 — arose from
the clear need for a broader and less technical treatment of the key topics
in statistical learning. In addition to a review of linear regression, ISLR
covers many of today’s most important statistical and machine learning
approaches, including resampling, sparse methods for classification and regression,
generalized additive models, tree-based methods, support vector
machines, deep learning, survival analysis, clustering, and multiple testing.
In recent years Python has become an increasingly popular language
for data science, and there has been increasing demand for a PythonLearning, With Applications in Python (ISLP), covers the same materials
as ISLR but with labs implemented in Python — a feat accomplished by the
addition of a new co-author, Jonathan Taylor. Several of the labs make use
of the ISLP Python package, which we have written to facilitate carrying out
the statistical learning methods covered in each chapter in Python. These
labs will be useful both for Python novices, as well as experienced users.
The intention behind ISLP (and ISLR) is to concentrate more on the
applications of the methods and less on the mathematical details, so it is
appropriate for advanced undergraduates or master’s students in statistics
or related quantitative fields, or for individuals in other disciplines who
wish to use statistical learning tools to analyze their data. It can be used
as a textbook for a course spanning two semesters.
Thể loại:Mathematics - Mathematical Statistics
Năm:2023
In lần thứ:1st
Nhà xuát bản:Springer
Ngôn ngữ:english
Trang:617
