The Elements of Statistical Learning PDF

Download The Elements of Statistical Learning PDF: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani & Jerome Friedman published on 1st January 2001. This book portrays the significant thoughts in these territories in a typical calculated system. While the methodology is statistical, the accentuation is on ideas rather than mathematics. However, it should be an important asset for analysts and anybody intrigued by information mining in science or industry. The book’s inclusion is expansive, from administered learning (forecast) to solo learning. The numerous points incorporate neural systems, bolster vector machines, grouping trees and boosting – the primary exhaustive treatment of this theme in any book.
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About the Authors:

Trevor Hastie, Robert Tibshirani & Jerome Friedman are professors of measurements at Stanford University. However, they are conspicuous scientists right now: and Tibshirani created summed up added substance models and composed a famous book of that title. Hastie co-grew a significant part of the statistical displaying software and the earth in R/S-PLUS and concocted chief bends and surfaces. Tibshirani proposed the tether and is co-creator of the extremely fruitful An Introduction to the Bootstrap. However, Friedman is the co-creator of numerous information mining devices including CART, MARS, projection interest and slope boosting.

Inside this book:

The field of Statistics is constantly challenged by the problems that science and industry bring to its door. In the early days, these problems often came from agricultural and industrial experiments and were relatively small in scope. With the advent of computers and the information age, statistical problems have exploded both in size and complexity. Challenges in the areas of data storage, organization and searching have led to the new field of “data mining”; statistical and computational problems in biology and medicine have created “bioinformatics.”
The challenges in learning from data have led to a revolution in the statistical sciences. However, in supervised learning, the goal is to predict the value of an outcome measure based on several input measures; in unsupervised learning, there is no outcome measure, and the goal is to describe the associations and patterns among a set of input measures.
However, Statistical learning plays a key role in many areas of science, finance, and industry. Here are some examples of learning problems: • Predict whether a patient, hospitalized due to a heart attack, will have a second heart attack. The prediction is to be based on demographics, diet and clinical measurements for that patient.

  •  Predict the price of a stock in 6 months from now, based on company performance measures and economic data.
  •  Identify the numbers in a handwritten ZIP code, from a digitized image.
  •  Estimate the amount of glucose in the blood of a diabetic person, from the infrared absorption spectrum of that person’s blood.
  •  Identify the risk factors for prostate cancer, based on clinical and demographic variables.

The science of learning plays a key role in the fields of statistics, data mining, and artificial intelligence, intersecting with areas of engineering and other disciplines. However, this book is about learning from data.

Download The Elements of Statistical Learning PDF


  • Book Name: The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  • Authors: Trevor Hastie, Robert Tibshirani & Jerome Friedman
  • Language: English
  • Status: Available
  • Number of Pages: 764 Pages
  • Download Format: PDF

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