Is Elements of Statistical Learning a good book?
Top positive review Very comprehensive, sufficiently technical to get most of the plumbing behind machine learning. Very useful as a reference book (actually, there is no other complete reference book). The authors are the real thing (Tibshirani is the one behind the LASSO regularization technique).
What are the elements of statistics?
Descriptive analysis and treatment of data, probability and probability distributions, statistical inferences, linear regression and correlations, chi-square, and some nonparametric statistics. Preexisting statistical computer programs may be used for some applications.
What are statistical learning methods?
Statistical Learning is a set of tools for understanding data. These tools broadly come under two classes: supervised learning & unsupervised learning. Generally, supervised learning refers to predicting or estimating an output based on one or more inputs.
How hard is statistical learning?
Statistics is challenging for students because it is taught out of context. Most students do not really learn and apply statistics until they start analyzing data in their own researches. The only way how to learn cooking is to cook. In the same way, the only way to learn statistics is to analyze data on your own.
What are data elements and why are they important?
Data element standards reduce data collection costs, and improve the consistency of data derived from different sources. The latter is essential for effective organizational decision-making. Data element standards are an essential component of information management.
What are data elements examples?
Any unit of data defined for processing is a data element; for example, ACCOUNT NUMBER, NAME, ADDRESS and CITY. A data element is defined by size (in characters) and type (alphanumeric, numeric only, true/false, date, etc.).
What is the goal of statistical learning?
The main goal of statistical learning theory is to provide a framework for study- ing the problem of inference, that is of gaining knowledge, making predictions, making decisions or constructing models from a set of data.
What is statistical learning give some examples?
Statistical learning plays a key role in many areas of science, finance and industry. A few examples are already considered in Lesson 1. Some more examples of the learning problems are: Predict whether a patient, hospitalized due to a heart attack, will have a second heart attack.
What is introduction in statistics?
Statistics is a mathematical science including methods of collecting, organizing and analyzing data in such a way that meaningful conclusions can be drawn from them. In general, its investigations and analyses fall into two broad categories called descriptive and inferential statistics.
What is an introduction to statistical learning?
An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data.
What is the best book to learn statistical learning?
“An Introduction to Statistical Learning (ISL)” by James, Witten, Hastie and Tibshirani is the “how to” manual for statistical learning. Inspired by “The Elements of Statistical Learning” (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods.
What are the best free eBooks to read for Statistics?
After taking a week off, here’s another free eBook offering to add to your collection. This time, let’s check out another classic of the genre, An Introduction to Statistical Learning, with Applications in R, written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.